Automation

One way that software can help improve your business is by automating tasks. This can free up time for you and your employees to focus on more important things. Many different types of software can help with automation. For example, there are project management software programmes that can automate tasks such as creating task lists, assigning tasks to employees, and tracking progress. There are also customer relationship management (CRM) software programmes that can automate tasks such as contact management, sales tracking, and marketing campaigns. The experienced developers from a reputable bespoke software development company suggest that you should carefully consider which tasks you want to automate. Not all tasks need to be automated. In some cases, it may be more efficient to do them manually.

Workflow Management

Another way that software can help improve your business is by improving workflow management. Workflow management is the process of organising and managing the steps involved in a task or project. It can help you to ensure that tasks are completed in a timely and efficient manner. There are many different types of workflow management software programmes. For example, there are task management software programmes that can help you to create and track task lists. These are the perfect tools for businesses that have multiple employees working on different tasks. There are also project management software programmes that can help you to manage and track the progress of projects. These are perfect for businesses that have large and complex projects. More often than not, businesses need a combination of both task management and project management software to efficiently manage their workflow.

Reporting And Analytics

Reporting and analytics mean being able to track the performance of your business. This can help you to identify areas where your business is doing well and areas where it could improve. There are many different types of reporting and analytics software programmes. For example, there are accounting software programmes that can help you to track your financial performance. There are also CRM software programmes that can help you to track your customer relationships. Just keep in mind that not all businesses need the same type of software. Some businesses may only need accounting software, while others may need a combination of accounting and CRM software. It all depends on the specific needs of your business.

Communication

Last but not the least, another important aspect of business that can be improved with software is communication. Good communication is essential for businesses of all sizes. It can help to improve employee morale, increase productivity, and reduce misunderstandings. Many different types of software can help with communication. For example, there are instant messaging software programmes that can be used for real-time communication between employees. There are also video conferencing software programmes that can be used for meetings and other events. Choosing the right type of communication software will depend on the needs of your business.

Choosing the right software for your needs

• Consider your business needs

One of the things you need to do when choosing software for your business is to consider your business needs. Not all businesses have the same needs. For example, a small business may not need as much software as a large corporation. This is because small businesses may only need to manage a few employees and a small number of customers. On the other hand, large businesses may need to manage hundreds or even thousands of employees and millions of customers. It’s important to carefully consider the specific needs of your business before making any decisions.

• Get input from employees

Another thing you need to do when choosing software for your business is to get input from employees. After all, they’re the ones who will be using the software. Ask them what type of software they need and why they need it. For instance, they may need software that can help them to be more productive or they may need software that can help them to communicate better with other employees. Getting input from employees can help you to make sure that you’re choosing the right type of software for your business.

• Think about the costs

Of course, you also need to think about the costs when choosing software for your business. Some software can be very expensive, so you need to make sure that you’re getting what you need without spending too much money. Once again, it’s important to consider the specific needs of your business. If you only need a few simple features, then you probably don’t need to spend a lot of money on software. On the other hand, if you need complex features, then you may need to spend more money. However, you may be able to take advantage of free or open source software if you’re on a tight budget.

• Research your options

Finally, you need to do some research before choosing software for your business. This is because there are many different types of software available and it can be hard to know which one is right for you. Talk to other businesses and see what type of software they’re using. You can also read online reviews to get an idea of the different options available. By doing so, you can make sure that you’re choosing the best software for your business.

Choose the right software for your business and you’ll be able to improve the way your business works. With the right software, you can take your business to the next level. Rest assured that with a little bit of research, you’ll be able to find the perfect software for your business needs.

James Johnston, Regional VP at Cloudera, presents a case for greater utilisation of data by banks and financial services firms.

For those who master the art of delivering customer service in financial services, there are huge rewards — including 55% higher returns for customer-centric banks. However, the financial services market is highly saturated, and challenger banks, of which there are 102 in the UK alone, are rivalling legacy institutions when it comes to giving consumers choices. In fact, Starling Bank, which only opened its doors in 2014, came out on top as one of the brands in the UK excelling at customer experience. So, how can financial institutions improve customer experience and remain competitive? One word: data.

True innovation lies in data

Organisations in the financial sector need to use data and analytics to offer their customers the most relevant products and services proactively. Currently, they do this by looking at traditional data sources, such as account activity, loan requests and investments. This helps these companies to form a complete understanding of the customer and their needs. However, for some banks, it is often here where their data analytic capabilities come to a halt. And it needs to change, as it is leaving them with only half the picture.

Today, there is a wider range of data sources available to banks than ever before, fuelled by the increasing amount of data we are producing. For example, unstructured data sources, including clickstream data, location data, social media streams and chatbots can provide a wealth of actionable intelligence. But by only analysing legacy sources, key insights into customers are lost. True innovation comes with the ability to analyse new and old data sources simultaneously. By doing so, banks can complete the picture of their customers and comprehensively anticipate and predict their needs based on their customer profile.

Organisations in the financial sector need to use data and analytics to offer their customers the most relevant products and services proactively.

Given the complexity and variety of traditional and newer sources of data, financial service providers need to ensure they have the tools in place to support them on their data journey. Gaining full visibility on every piece of data flowing through their network, from a single toolset, regardless of where it resides or where it came from, is therefore critical. By implementing this level of visibility, data can be analysed, and the true value derived in real-time to the benefit of the customer. The ability to detect fraud is a perfect example of this. Suppose a bank can ingest and analyse data in the here and now. In that case, the business can identify patterns and trends that are indicative of fraud and alert the customer to this activity, before it becomes an issue.

Embracing a customer-centric approach

It’s clear that the way data is linked together, in a data lifecycle, is what enables organisations to derive intelligence through which exceptional customer experience is delivered. This is why focusing on developing a connected data lifecycle, which takes into account the holistic view of the entire data journey from edge to cloud, will become a cornerstone of success for banks who want to lead in their industry.

A connected data lifecycle will, however also help banks and other financial services organisations to can meet critical business goals, including:

Acquiring new customers

Segmentation allows businesses to analyse and profile their current customers. By leveraging techniques such as segmentation, companies can fine-tune their outreach and target prospective customers by taking insights and creating messaging that is tailored to target new customers.

Expanding existing business

With a complete picture of the customer, including every interaction they have with the organisation, banks can look for opportunities to offer new products and services proactively. When looking for opportunities to cross-sell, it is important that the salesperson has access to the customer’s entire profile, including previous searches and history, in order to offer the most relevant product. If the different data sets relating to the customer are sat in silos based on how they were ingested into the business, it can delay this process, and the customer may lose interest or look elsewhere.

With a complete picture of the customer, including every interaction they have with the organisation, banks can look for opportunities to offer new products and services proactively.

Driving customer loyalty and long-term retention

Using analytics-driven customer engagement tools, such as digital assistants, customer surveys and feedback analysis, financial institutions can gather this information, derive insights from it in real-time and then push the outcomes back to their customers. It is a quick and effective way to garner a deep understanding of customers’ needs and personalising offerings accordingly. In fact, continuous re-evaluation of the data could quite literally allow companies to give customers what they want, despite their ever-changing needs.

Putting data insights into practice

The focus on customer experience is a critical component to a financial services organisation retaining loyal customers and remaining competitive. Two premier businesses that have remained at the forefront of their industry because of their use of data are Rabobank and DBS.

In order to help its customers — including small businesses — become more self-sufficient and improve debt settlement, Rabobank needed access to a varied mix of high-quality, accurate and timely data, that they could feedback to customers on demand. To achieve this, Rabobank implemented technologies that can cope with heavy pressures on data processing and ingest large quantities of streaming data. This gave Rabobank the ability to rapidly process historic and real-time data together, helping its employees run faster queries. From customers’ loan repayment patterns to up-to-the-minute transaction records, Rabobank and its customers can now immediately access the valuable data needed to help them understand the status of their financial situation.

DBS, one of the leading banks in Asia, recognised that in order to deliver superior customer experience it had to become more data-driven. However, the company’s traditional technology stack for supporting advanced analytics was expensive to scale and not flexible enough to support this work. This led the bank to build a central data team and enterprise data hub that now enables staff to continually experiment and be at the forefront of innovation, to understand the customer experience more fully. DBS then used data and knowledge to apply human-centred design to its services. With the ability to more easily store and analyse billions of events in a modern data platform, DBS can answer questions before they’re asked, effectively engaging customers and proactively delivering a better service.

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Utilise data, remain competitive

Competition amongst financial institutions is fierce, and one negative experience is all it can take for a demanding, and changing, consumer to jump ship. To avoid this, financial services companies need to be guided by their data. The organisations that excel in the future will be the ones that are investing in the means to effectively ingest, analyse and derive value from data and put these insights into practice. After all, the data is there to be analysed and acted upon, so financial services organisations need to ensure they are getting the most out of it. If they don’t, they risk losing out to the more customer-centric competition.

Kris Sharma, Finance Sector Lead at Canonical, explores the value of open source technologies in steering financial services through times of disruption.

In a post-Brexit world, the industry is facing regulatory uncertainty at a whole different scale, with banking executives having to understand the implications of different scenarios, including no-deal. To reduce the risk of significant disruption, financial services firms require the right technology infrastructure to be agile and responsive to potential changes.

The role of open source

Historically, banks have been hesitant to adopt open source software. But over the course of the last few years, that thinking has begun to change. Organisations like the Open Bank Project and Fintech Open Source Foundation (FINOS) have come about with the aim of pioneering open source adoption by highlighting the benefits of collaboration within the sector. Recent acquisitions of open source companies by large and established corporate technology vendors signal that the technology is maturing into mainstream enterprise play. Banking leaders are adopting open innovation strategies to lower costs and reduce time-to-market for products and services.

Banks must prepare to rapidly implement changes to IT systems in order to comply with new regulations, which may be a costly task if firms are solely relying on traditional commercial applications. Changes to proprietary software and application platforms at short notice often have hidden costs for existing contractual arrangements due to complex licensing. Open source technology and platforms could play a crucial role in helping financial institutions manage the consequences of Brexit and the COVID-19 crisis for their IT and digital functions.

Open source software gives customers the ability to spin up instances far more quickly and respond to rapidly changing scenarios effectively. Container technology has brought about a step-change in virtualisation technology, providing almost equivalent levels of resource isolation as a traditional hypervisor. This in turn offers considerable opportunities to improve agility, efficiency, speed, and manageability within IT environments. In a survey conducted by 451 Research, almost a third of financial services firms see containers and container management as a priority they plan to begin using within the next year.

Open source software gives customers the ability to spin up instances far more quickly and respond to rapidly changing scenarios effectively.

Containerisation also enables rapid deployment and updating of applications. Kubernetes, or K8s for short, is an open-source container-orchestration system for deploying, monitoring and managing apps and services across clouds. It was originally designed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes is a shining example of open source, developed by a major tech company, but now maintained by the community for all, including financial institutions, to adopt.

The data dilemma

The use cases for data and analytics in financial services are endless and offer tangible solutions to the consequences of uncertainty. Massive data assets mean that financial institutions can more accurately gauge the risk of offering a loan to a customer. Banks are already using data analytics to improve efficiency and increase productivity, and going forward, will be able to use their data to train machine learning algorithms that can automate many of their processes.

For data analytics initiatives, banks now have the option of leveraging the best of open source technologies. Databases today can deliver insights and handle any new sources of data. With models flexible enough for rich modern data, a distributed architecture built for cloud scale, and a robust ecosystem of tools, open source platforms can help banks break free from data silos and enable them to scale their innovation.

Open source databases can be deployed and integrated in the environment of choice, whether public or private cloud, on-premise or containers, based on business requirements. These database platforms can be cost-effective; projects can begin as prototypes and develop quickly into production deployments. As a result of political uncertainty, financial firms will need to be much more agile. And with no vendor lock-in, they will be able to choose the provider that is best for them at any point in time, enabling this agility while avoiding expensive licensing.

As with any application running at scale, production databases and analytics applications require constant monitoring and maintenance. Engaging enterprise support for open source production databases minimises risk for business and can optimise internal efficiency.

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Additionally, AI solutions have the potential to transform how banks deal with regulatory compliance issues, financial fraud and cybercrime. However, banks need to get better at using customer data for greater personalisation, enabling them to offer products and services tailored to individual consumers in real time. As yet, most financial institutions are unsure whether a post-Brexit world will focus on gaining more overseas or UK-based customers. With a data-driven approach, banks can see where the opportunities lie and how best to harness them. The opportunities are vast and, on the journey to deliver cognitive banking, financial institutions have only just scratched the surface of data analytics. But as the consequences of COVID-19 continue and Brexit uncertainty once again moves up the agenda, moving to data-first will become less of a choice and more of a necessity.

The number of data sets and the diversity of data is increasing across financial services, making data integration tasks ever more complex. The cloud offers a huge opportunity to synchronise the enterprise, breaking down operational and data silos across risk, finance, regulatory, customer support and more. Once massive data sets are combined in one place, the organisation can apply advanced analytics for integrated insights.

Uncertainty on the road ahead

Open source technology today is an agile and responsive alternative to traditional technology systems that provides financial institutions with the ability to deal with uncertainty and adapt to a range of potential outcomes.

In these unpredictable times, banking executives need to achieve agility and responsiveness while at the same time ensuring that IT systems are robust, reliable and managed effectively. And with the option to leverage the best of open source technologies, financial institutions can face whatever challenges lie ahead.

With the entire industry currently under pressure due to uncertainty, data must lie at the core of every decision any business makes if it wants to succeed. In fact, research from McKinsey tells us organisations that leverage customer behavioural data and insights outperform peers by 85% in sales growth and more than 25% in gross margin. Jil Maassen, lead strategy consultant at Optimizely, offers Finance Monhly her thoughts on how data experimentation can be used to drive financial services forward.

The game-changing nature of data

One of the best examples of risk and reward, based on data science, comes from the world of baseball. Back in 2002, Billy Beane, general manager of the unfancied Oakland Athletics baseball team, spawned an analytical arms race among US sports teams. Working under a limited budget, Beane used obscure stats to identify undervalued players — eventually building a team that routinely beat rivals who had outspent them many times over.

Data analytics turned the game on its head by proving that data is an essential ingredient for making consistently positive decisions. The success of the bestselling book and subsequent Oscar-winning film, Moneyball, based on Beane’s story, took data analytics mainstream. Today, financial services companies are applying a “Moneyball” approach to many different aspects of their business, especially in the field of experimentation.

Data analytics turned the game on its head by proving that data is an essential ingredient for making consistently positive decisions.

We live in testing times

Experimentation departments for the purposes of testing, also known as Innovation Labs, have been growing at a prolific rate in recent years, with financial services seeing the highest rate of growth according to a survey by Capgemini. By the end of 2018, Singapore alone had 28 financial service-related Innovation Labs. Alongside this, research from Optimizely reports that 62% of financial services companies plan to invest in both better technology and skilled workers for data analytics and experimentation.

Areas such as fund management are no strangers to data analytics. But since the fintech disruptors arrived on the financial services scene, legacy banks are now using data in combination with experimentation to evolve other elements of their business and remain competitive. Many have found that this is helping them to address common concerns, including how to improve customer experience and successfully launch products to market. So much so, that our research found that 92% of financial services organisations view experimentation as critical to transforming the digital customer experience. In addition, 90% also consider experimentation key to keeping their business competitive in the future.

Eat, sleep, test, repeat

However, experimentation takes patience. As Billy Beane said when his strategies didn’t deliver right out of the gate: “It's day one of the first week. You can't judge just yet.” He was ultimately vindicated. Like any new initiative, experiments can fail because of cultural “organ rejection.” They require taking short-term risks that don’t always work, all in service of long-term learning. It’s the job of Innovation Labs to take these risks, and often, one for the team, by being prepared to fail.

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The point is, when you're transforming something and making massive change, not everyone is going to understand right away. The best way to convince people that your theory is correct is to show them — not tell them — you're right. Experimentation initiatives in business, and especially in financial services where risks and rewards have high impact and return, allow new ideas to be proven right before they play out in front of a paying public.

Founded in facts and stats, experimentation promotes an ethos that is key in adopting new technologies and utilising data analytics to build roadmaps for the future. As the amount of data companies have access to increases, the ethos of experimentation will only become more important for predicting and changing the future for the better.

Experimentation is about measuring and learning and repeating that process until optimum results are achieved. The final word in this regard should perhaps go to Beane himself; “Hard work may not always result in success. But it will never result in regret.” His story is something that all financial services organisations can learn from.

Income Analytics today announces the launch of its tenant income risk indices and benchmarks – a new and unique set of indicators for quantifying tenant income risk using data on over 355 million global companies from leading global provider of business decisioning data and analytics, Dun & Bradstreet.

Income Analytics redefines how the global real estate industry can access, analyse and deploy company credit data on tenants, real estate assets and investment portfolios, enable real estate professionals, investors and lenders to receive ‘real time’ analysis of underlying tenants creditworthiness and, with confidence, appraise anticipated future performance and ultimately likelihood of default.

Income Analytics reports and dashboards incorporate new proprietary analytical tools and scoring (INCAN scores) alongside the existing credit report data including:

• 10-year tenant income risk forecast
• Global tenant scores
• Bond equivalent ratings
• Global corporate family tree

As a global institutional asset class (US$30.2trn¹), real estate requires the same analytical analysis as equities and bonds. As stated by Andrew Baum, Professor of Practice, Said Business School, University of Oxford: “The value of real estate investment is ultimately determined by the level, duration and quality of the rental income paid by your tenants.”

Income Analytics provides a range of tools comprising:

• Tenant credit reports – Identification checks, quality of covenant and probability of default along with the usual company financials;
• Asset level credit reports – Aggregated income stream analysis and assessment of probability of loss; and
• Portfolio Income Manager – A platform to view and monitor the financial health of existing tenant and portfolio income quality 24/7.

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Matthew Richardson, Co-founder and Chief Executive Officer of Income Analytics commented: “At Income Analytics, we have created a truly unique and much needed set of tools and analytics for the global commercial real estate industry. No industry specific product for investors and lenders to assess and monitor the changing quality of their tenant income over time currently exists. More worrying is that very little has changed since the sub-prime crisis of 2008 and the recent global crisis caused by Coronavirus makes the need to access accurate and current income data more important than ever before. Our aim is to provide the real estate industry with a critical tool in which to assist investment decisions and investor reporting.”

Maxwell James, Chairman of Income Analytics stated: “Income Analytics has created a new and world class set of indices and benchmarks for the commercial real estate market. The insight that these measures bring is already resulting in better informed investment decisions by our existing clients. The application of these analytical methods offers the potential for investors and lenders to greatly enhance transparency and risk appraisal of portfolios or loan books at this critical time.”

Edgar Randall, Commercial Director UK & Ireland, Dun & Bradstreet commented: “Dun and Bradstreet is delighted to be partnering with Income Analytics to provide commercial data and analytics that support innovation and digital transformation across the real estate industry. Our aim is to provide a comprehensive risk solution for commercial real estate teams by combining our data with Income Analytics’ expertise and new platform to deliver actionable insights to drive business performance.”

Income Analytics was founded by and is led by an award-winning management team with unique experience and a strong track record in data monetisation and analytics in the commercial real estate sector. Biographies can be viewed at the company's website.

In light of the current COVID-19 situation the formal launch event has had to be postponed but details will follow in due course.

Finance teams are still spending too much time in ‘excel hell.’ Every hour spent grappling with spreadsheets, pivot tables, and pie charts are hours that could be spent helping make better business decisions. And yet, astonishingly, top finance functions are still devoting 75% of their time to data analysis, according to a recent PWC study. Eugene Hillery, Senior Director of International Operations at Tableau, offers Finance Monthly his thoughts on the issue and why it should be turned around.

Spreadsheet drudgery isn’t just frustrating and inefficient, it’s outdated. There is a huge range of intuitive, interactive and highly visual data software available – what some call ‘visual analytics’ - designed to help knowledge workers see and analyse the data that matters to them, faster.

Delivering insight from data should be the core competence of finance – not spreadsheet navigation. Yet, research from Sage shows two thirds of CFOs (64 %) are still unable to make data-driven decisions to drive business change. Here are five reasons to kick-off an analytics overhaul:

1. You Can Work (And Collaborate) From a Single Source of Truth

Conventional spreadsheets are capable of handling many tasks, but real time collaboration has never been their strongest suit.

Inconsistent version control, restricted server access and unnecessary duplication are a drag on far too many finance teams. When there are multiple sources of ‘truth’, hours of time are needed to make sure conclusions are built on accurate and up-to-date data. The longer this process takes, the less value you can claim from any time-sensitive data.

With more advanced analytics products, finance teams can bring diverse data sets together from across an entire organisation, allowing everyone to work from a single source of truth. This offers a holistic view and saves time especially when everyone, whether from AP, AR, Tax or Purchasing can collaborate on the same data in ‘real time’.

Inconsistent version control, restricted server access and unnecessary duplication are a drag on far too many finance teams.

2. You Can Get Insight Overnight

More than ever, the ability to connect to offices around the world is a business necessity. The power of a rolling international handover between knowledge workers using accurate, up-to-date data, is tremendous.

For example, if daily sales or staff performance data is be collected at the close of a business day in London, it can be turned into insight by teams in the US literally overnight. This means recommendations for action land on desks at the start of the next day in the UK, and issues can be resolved faster.

If a coherent view of your accounts means drawing information from data sources in China and the US, for example, trying to reconcile them through different spreadsheets will only bury insight. Quick answers are critical for teams operating across different time zones, as for any business that needs an accurate overview of what’s going on in a hurry.

When diverse data sources are unified in a single interactive dashboard, drilling into the numbers can be done by anyone, wherever they are.

3. You Can See Both Granular Detail and the Big Picture

Managing business expenses is a never-ending task, but it’s another area where working smarter beats working harder.

Data analytics software helps uncover the kind of hard to spot correlations that can be invaluable in finding new ways to keep costs down. Dashboards should make it easy, for example, to see which employees are in the habit of booking flights well in advance (saving the company money) and those who rack up huge bills by making last minute purchases.

A faster understanding of data outliers is also valuable in the quick response to business challenges that may exist. Instead of questioning ‘what’ is happening, conversations are led with ‘why’ it is happening. Data analytics makes it easier to uncover cost drivers and make predictions about cash flow. This equips finance teams to identify the source of a challenge faster than ever and help drive the solution.

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 4. You Can Put Your Focus on the Future

Access to an organisation’s accounting full history means the finance team is best placed to offer predictions for its future. In general, the richer and more diverse the data that underpins those forecasts, the more accurate and useful they become.

With data analytics, finance teams can use a cash flow summary dashboard to help management understand the outlook in aggregate. They can ask useful questions like “what are our balances by currency, subsidiary, country, banking partner or geography?” The ability to reveal and answer these is fundamental to supporting other financial processes like preparing for audits and SOX compliance.

Combining effective data analytics and artificial intelligence support allows teams to compile and comprehend far bigger data sets, and even help present larger, more evidence-laden projections. This level of authority is what enables finance teams to play a more strategic role in the boardroom - advising CEOs, boards and investors, not to mention staff or customers. In fact, eight in 10 CFOs in the UK (78 %) say their role has changed recently and they are focusing more time and effort on business-wide operational transformation, according to Accenture.

Access to an organisation’s accounting full history means the finance team is best placed to offer predictions for its future.

The best visual analytics software make comparisons between external data sources like economic trends, and internal sources like operational numbers or sales figures. This in turn empowers finance teams to be more efficient and intuitive, making better recommendations with longer lasting impact.

5. Investing in Your Money People

The pace and scale of digital transformation is something finance teams understand better than most. After all, they are the ones processing payments for every major IT investment a company makes.

It’s not surprising then that it is so frustrating to see finance teams often overlooked for technology investments which could in fact create efficiencies that drive business forward.

Of all business areas that stand to benefit from the ongoing revolution in data analysis, finance departments have the most to gain. Gartner research shows that the number of finance departments deploying advanced analytics will double within the next three years. Visual and AI-empowered analytics can untap the insight and creativity currently locked in finance teams across the UK – but only if they can look up from their spreadsheets and see them.

It’s estimated that 81% of finance teams are currently undergoing finance transformation, yet research by Gartner reveals that seven out of 10 finance transformations fail.

This article, authored by Laura Timms, Product Strategy Manager at MHR Analytics, based on the new finance analytics guide from MHR Analytics, will reveal the benefits of adopting analytics to supercharge your efforts and help ensure that your finance transformation is a successful one.

Finance strategy that’s aligned with future business needs

Transformation is more than simply hitting a financial goal. It’s about being able to respond to the current and future needs of the organisation – something that can only be achieved when finance is connected to the wider business.

Unfortunately, with all of the demand that finance teams receive, it can be easy to fail to recognise how financial activity translates into everyday business.

This can lead teams working introspectively, which can quickly translate into silos, with poor communication of information, lower levels productivity and consequently a less valuable finance team.

To prevent this from happening, the financial strategy needs to be aligned with activity across the business, and analytics provides the platform to do exactly that.

Using a data warehouse, data from across the organisation can be synced to give finance teams real-time insights into how changes in one area of the business will impact the course of action they take.

This means that finance teams are able to steer away from getting caught up in metrics like historical spend and industry benchmarks, and are instead grounded in how the finance strategy relates to the unique needs of their business.

Using a data warehouse, data from across the organisation can be synced to give finance teams real-time insights into how changes in one area of the business will impact the course of action they take.

1. Focus on high-value tasks

According to Gartner, 56% of companies are in the evaluation phase of adopting AI to automate accounting & finance processes. By 2020 it’s estimated that 31% of companies will have actually implemented this into their business and 26% in “operating” mode, where AI is actively used in accounting & finance processes.

But what does this mean for finance transformation?

Well, AI technology is providing a platform that is changing the role of finance teams at a rapid pace. Through automating tedious financial processes, finance teams no longer have to spend their time buried in spreadsheets.

Everything from cash disbursement, revenue management and general accounting could be automated through leveraging analytics – in fact, it’s estimated that up to 40% of financial activity could be automated, and another 17% mostly automated.

Research goes on to reveal that for an accounting team with 40 full-time employees, with an average salary of £60,000 would save around 25,000 hours and nearly £72,000 that would have otherwise been wasted on team members carrying out repetitive tasks.

This time saved can instead be spent on higher-value tasks that facilitate business transformation and allow finance to act as a trusted strategic partner to the business.

2. Understand where to allocate resources

Sometimes it can feel like finance are caught in the middle, with demands left, right and centre of the business. And with eloquent justifications from each department explaining why their project should be prioritised, it can leave finance teams stretched under the pressure to please everyone.

Analytics works to hand back the power to finance teams.

Through interactive dashboards that display performance across the business, finance teams are able to easily identify the key value drivers of financial growth.

This means that they’re able to present stakeholders with “the facts” and justify financial activity, only spending resources on activities that generate the most financial value, whilst cutting unnecessary costs.

On top of this, finance teams can look internally to see what they’re spending their own time and resources on. This can help them to define their list of roles and responsibilities as a department to ensure that they don’t get caught up in low-value tasks.

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3. Make faster, more reliable decisions

At the core of any finance transformation is the need to adapt finance practices to meet increasing business demand.

Despite this, many finance teams are still relying on outdated methods to carry out financial processes.

Relying on spreadsheets to communicate and understand what’s going on in the wider business is a common theme amongst finance, but using manual methods alone leaves room for human error.  In fact, research shows that nearly 90% of spreadsheets contain errors, and this can make it tricky to make decisions with confidence.

This approach also means that teams are often forced to spend hours analysing data and pulling reports. This can lead to lags in getting all-important insights, which delays decision making and can result in “in hindsight” discussions with stakeholders.

Analytics works to streamline financial processes to provide teams with fast and accurate insights at the touch of a button. Through real-time data and automation of once tedious processes, teams can see bumps in the road way in advance and have greater confidence in their decisions.

Sources:

https://emtemp.gcom.cloud/ngw/globalassets/en/finance/documents/trends/cfo-journal-new-digital-workforce.pdf

https://www.gartner.com/en/confirmation/finance/trends/new-digital-workforce

https://emtemp.gcom.cloud/ngw/globalassets/en/finance/documents/insights/hallmarks-of-winning-finance-transformations.pdf

https://www.gartner.com/en/confirmation/finance/insights/finance-transformation

https://emtemp.gcom.cloud/ngw/globalassets/en/finance/documents/insights/defining-the-scope-of-fpa-analytic-support.pdf

https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-us-da-3minFinanceAnalytics.pdf

[2] https://www.mckinsey.com/business-functions/operations/our-insights/new-technology-new-rules-reimagining-the-modern-finance-workforce

https://www.blackline.com/blog/account-reconciliations/3-audit-benefits/?ite=1192&ito=1771&itq=b820f4c8-2db4-44be-bdbf-1230cc9fa177&itx%5Bidio%5D=522393

[1] https://emtemp.gcom.cloud/ngw/globalassets/en/finance/documents/trends/cfo-journal-new-digital-workforce.pdf

[3] https://www2.deloitte.com/gr/en/pages/finance-transformation/topics/finance-transformation.html

https://openviewpartners.com/blog/finance-transformation-the-art-of-getting-more-from-your-finance-team/#.XSxsDuhKiUk

https://www.gartner.com/en/finance/insights/finance-transformation

This week Finance Monthly hears from Caroline Hermon, Head of Adoption of Artificial Intelligence and Machine Learning at SAS UK & Ireland, on the adoption of open source analytics in the finance sector and beyond.

Open source software used to be treated almost as a joke in the financial services sector. If you wanted to build a new system, you bought tried and tested, enterprise-grade software from a large, reputable vendor. You didn’t gamble with your customers’ trust by adopting tools written by small groups of independent programmers. Especially with no formal support contracts and no guarantees that they would continue to be maintained in the future.

Fast-forward to today, and the received wisdom seems to have turned on its head. Why invest in expensive proprietary software when you can use an open source equivalent for free? Why wait months for the official release of a new feature when you can edit the source code and add it yourself? And why lock yourself into a vendor relationship when you can create your own version of the tool and control your own destiny?

Enthusiasm for open source software is especially prevalent in business domains where innovation is the top priority. Data science is probably the most notable example. In recent years, open source languages such as R and Python have built an increasingly dominant position in the spheres of artificial intelligence and machine learning.

As a result, open source is now firmly on the agenda for decision makers at the world’s leading financial institutions. The thinking is that to drive digital transformation, their businesses need real-time insight. To gain that insight, they need AI. And to deliver AI, they need to be able to harness open source tools.

The open source trend encompasses more than just the IT department. It’s spreading to the front office too. Notably, Barclays recently revealed that it is pushing all its equities traders to learn Python. At SAS, we’ve seen numerous examples of similar initiatives across banking domains from risk management to customer intelligence. For example, we’re seeing many of our clients building their models in R rather than using traditional proprietary languages.

A fool’s paradise?

However, despite its current popularity, the open source software model is not a panacea. Banks should still have legitimate concerns about support, governance and traceability.

The code of an open source project may be available for anyone to review. But tracing the complex web of dependencies between packages can quickly become extremely complex. This poses significant risks for any financial institution that wants to build on open source software.

Essentially, if you build a credit risk model or a customer analytics application that depends on an open source package, your systems also depend on all the dependencies of that package. Each of those dependencies may be maintained by a different individual or group of developers. If they make changes to their package, and those changes introduce a bug, or break compatibility with a package further up the dependency tree, or include malicious code, there could be an impact on the functionality or integrity of your model or application.

As a result, when a bank opts for an open source approach, it either needs to put trust in a lot of people or spend a lot of time reviewing, testing and auditing changes in each package before it puts any new code into production. This can be a very significant trade-off compared to the safety of a well-tested enterprise solution from a trusted vendor. Especially because banking is a highly regulated industry, and the penalties for running insecure or noncompliant systems in production are significant.

What use is power without control?

When it comes to enterprise-scale deployment, open source analytics software also often poses governance problems of a different kind for banks.

Open source projects are typically tightly focused on solving a specific set of problems. Each project is a powerful tool designed for a specific purpose: manipulating and refining large data sets, visualising data, designing machine learning models, running distributed calculations on a cluster of servers, and so on.

This “do one thing well” philosophy aids rapid development and innovation. But it also puts the responsibility on the end user – in this case, the bank – to integrate different tools into a controlled, secure and transparent workflow.

As a result, unless banks are prepared to invest in building a robust end-to-end data science platform from the ground up, they can easily end up with a tangled string of cobbled-together tools, with manual processes filling the gaps.

This quickly becomes a nightmare when banks try to move models into production because it is almost impossible to provide the levels of traceability and auditability that regulators expect.

Language doesn’t matter

The good news is that there’s a way for banks to benefit from the key advantages of open source analytics software – its flexibility and rapid innovation – without exposing themselves to unnecessary governance-related risks.

The language a bank’s data scientists choose to write their code in shouldn’t matter. By making a clean logical separation between model design and production deployment, banks can exploit all the benefits of the latest AI tools and frameworks. At the same time, they can keep their business-critical systems under tight control.

SAS plus open source

One SAS client, a large financial services provider in the UK, recently took this exact approach. The client uses open source languages to develop machine learning models for more accurate pricing. Then it uses the SAS Platform to train and deploy models into full-scale production. As a result, model training times dropped from over an hour to just two and a half minutes. And the company now has a complete audit trail for model deployment and governance. Crucially, the ability to innovate by moving from traditional regression models to a more accurate machine learning-based approach is estimated to deliver up to £16 million in financial benefits over the next three years.

Amazon was once a small business selling books on the internet. Now it’s at the top of its game, with its hands in a multitude of baskets. Surely there’s a wide variety of lessons we can learn from their dynamic strategies. Below, Karen Wheeler, Vice President and Country Manager UK at Affinion, presents Finance Monthly with a guide to Amazon’s operations through the eyes of financial organizations.

It’s rare to meet someone who has never used the world’s largest internet retailer, Amazon. Whether it’s conquering Christmas lists, watching boxsets through Prime or managing life admin through the intelligent personal assistant Alexa, its offerings are endless.

This extensive list of services and benefits that are all designed around user convenience, simplicity and enhanced customer experience is one of the biggest contributing factors to its success.

Financial organisations, however niche or specialist, can take a leaf out of Amazon’s book when it comes to engaging with customers and harnessing innovative solutions to continuously improve their offering.

Here are five lessons financial firms such as banks and insurance companies can learn from Amazon.

1. Put the customer at the forefront of any business model

Listening to what the customer wants has been the driving force behind many of Amazon’s products and developments. McKinsey’s CEO guide to customer experience advises that the strategy “begins with considering the customer – not the organisation – at the centre of the exercise”.

This can often be quite a challenging ethos for the financial services sector to buy into, particularly for the more traditional bricks-and-mortar companies where the focus is often on the results of a new initiative, rather than the journey the company must take its customers on to get there.

It’s a case of convincing senior management that the initiative is a risk worth taking and just requires some patience. Amazon originally launched Prime as an experiment to gauge customers’ reactions of ‘Super Saver Shipping’ and it was predicted to flop. Nowadays it’s one of the world’s most popular membership programmes, generating $3.2bn (£2.3bn) in revenue in 2017, up 47 per cent from 2016.

2. Don’t wait to follow a disruptive competitor

To stay ahead of the curve amidst the flurry of fintech start-ups, financial organisations need to come up with their own innovative customer experience solutions, rather than allow newcomers to do so first and then follow suit.

From the customer’s perspective, a proactive approach will always go down better than a reactive one. Amazon CEO Jeff Bezos has previously spoken about tech companies obsessing over their competitors and waiting for them launch something new so that they can ‘one-up’ it. He once wrote: “Many companies describe themselves as customer-focused, but few walk the walk. Most big technology companies are competitor focused. They see what others are doing, and then work to fast follow.”

What sets Amazon apart is listening to what the customer wants and prioritising them over competitors.

A great example in the insurance sector is US digital insurer Lemonade, who last year set a world record for the speed and ease of paying out on a claim of just three seconds. This was done through its AI virtual assistant ‘Jim’ and has helped to kickstart a new trend of using AI in the industry. Ultimately, Lemonade listened to the masses in that most of us see shopping around for insurance and filing claims as complicated and admin-heavy. A quick, simple, paperless alternative would no doubt result in increased customer loyalty and, in turn, increased profits.

3. Analytics are key for personalisation

It’s no secret that Amazon is one of the leaders that has paved the way for analytics. It’s through the company recognising the need for them which has led to customers becoming accustomed to personalisation and expecting it as soon as they have had their first interaction with a business.

Financial organisations are no exception to this and, while it may seem like a scary commitment to more traditional firms, it doesn’t have to be complicated. A classic, simple example is Amazon storing customers’ shopping habits and sending them prompts for new products similar or related to those they have purchased in the past.

In the financial world, digital bank Monzo is leading the charge by monitoring customers’ spending habits to offer them financial advice to help them save money and budget responsibly. For example, its data once showed that 30,000 of its customers were using their debit cards to pay for transport in London – so Monzo can advise them they could save money if they invested in a year-long travel card, for instance.

There are endless things financial organisations can do using customer data to provide the customer with an experience unique to them, rather than continuing to make them feel like just another cog in the wheel. At Affinion we believe in ‘hyper-personalisation’, in that these days it’s no longer good enough to just know a customer’s history of transactions with a company and when their birthday is.

Customers are getting more tech-savvy by the day and are expecting real-time responses with a deep insight into their interactional behaviour – they won’t remain engaged if follow up contact is irrelevant and untargeted. Customer engagement has moved on from companies communicating to the masses, it’s about creating tailored, intuitive relationships with them on an individual basis.

4. Venture out into new areas

The way we live as a society is forever changing and, as we get busier and busier, any small gesture to make life that little bit easier goes a long way. The consolidation of services such as banking, insurance, mobile phone networks, utilities and shopping is a great way to ensure customers remain loyal to a brand as it will – if done right – add value and reduce hassle to their lives.

As an expert at disrupting industries, Amazon has taken note of this growing need for convenience over the years and has expanded its offering for customers, allowing them to carry out multiple day-to-day tasks with one account. In the last few months alone, Amazon has hinted that it may acquire a bank to break into the financial industry and potentially start its own healthcare company.

Regardless of size, financial organisations should always be looking for new areas they could tap into to broaden their offering and show customers that their needs are at front of mind.

5. Always go above and beyond

A rising factor in the way that customers align themselves to a brand is its stance on ethical issues and its contributions back into society. It’s a shift that seems to be most prominent with Generation Y, as the Chartered Institute of Marketing found that 81 per cent of millennials expect companies to make a public commitment to good corporate citizenship and nine in 10 would switch brands to one associated with a good cause.

Amazon has gone that one step further, with its AmazonSmile initiative that allows the customer to choose a charitable organisation that it will donate 0.5% of eligible purchases to. Not only does this show Amazon’s commitment to charitable causes, it gives the customer control of where their money ends up.

This is an easy win for the financial sector, given that one of its sole purposes is to look after money and move it around. For firms that target younger generations in particular, looking at ways to involve customers in charitable donations in a fun, transparent and seamless way is a no-brainer for increasing loyalty and advocacy.

Always a chore, never a pleasure

For many people, personal finance is perceived as a chore and often quite complicated. Improving the customer experience and building in programmes to engage them can help greatly with this and financial organisations need to adopt the ‘customer first’ ethos that Amazon showcases so effortlessly. With new fintech disruptors creeping into view, keeping customers loyal has never been so important.

Numa Solution is a financial analytics solutions provider that offers bespoke analytics solutions, in addition to readymade solutions for Financial Consolidation, Activity Based Costing and IFRS9. Some of the company’s bespoke analytics solutions include Hydrocarbon Fiscal Analytics, Risk Analytics and Economics Data Management. Numa provides analytics solutions to clients in a variety of industries, including oil and gas, financial services, pension funds, regulators, logistics, manufacturing, utilities, pharmaceutical and more. The company’s financial analytics solutions are developed to ease the implementation and usage, so the users can focus on the more important and value added tasks.

 Numa is an IBM Gold Business Partner and is also a part of the Business Associates Consulting (BAC) group of companies - a management consulting practice. This month, Finance Monthly reached out to the company’s Founder and Managing Director Azhim Hadi Daud to hear more about Numa Solution’s beginnings, services and clients.

What are Numa Solution’s mission, ethics and values?

Our mission at Numa is to be one of the leading analytics solutions provider. We aim to facilitate and improve the business analytics processes within organisations and to provide greater insights of their businesses.

What was the process of setting up the Group like?

The initial years were difficult, as we had to attract customers and close projects to ensure our survival. At the same time, we had to build our portfolio of clients and project references. Another challenging aspect was also building the support system for the company, i.e. people, financials, infrastructure etc.

How did you attract your first customers?

We did some cold calls on targeted companies with our service offerings. We also approached potential clients, with relationships to propose for our services.

We started Numa as a technology enabler to provide value in providing immediate implementable solutions to customers. This provided an edge over other management consulting firms which offer only advisory services.

What does your role as a Managing Director involve? What are the day-to-day challenges that you’re faced with?

My role is to drive the business forward, especially into new growth areas, on top of ensuring all functions of the company are running smoothly, in order to support the business.

One of the main challenges is that are face is connected to continuously developing our people with the appropriate capabilities, so they can reflect Numa’s growth.

What is your vision for the future of Numa Solution? Where would you like your business to be in three years?

We envision that Numa would be one of the leading financial analytics solutions provider around.

In order to achieve that goal, we are developing more financial analytics solutions offerings and planning for further geo expansions into other regions, namely ASEAN, MENA and more. We are also looking for tie-ups or partnerships with resellers to expand our product reach globally.

Do you have any upcoming plans or projects you would be willing to share with us?

We are currently working on developing solutions for ‘IFRS 15 – Revenue’ and ‘IFRS 17 – Insurance Contracts’ which will hopefully be ready by Q1 and Q2 2018. This should be timely for clients who are looking for financial analytics solutions to facilitate them in the implementations of these IFRS standards.

Contact details:

• Email: azhim@numasolution.com, info@numasolution.com
• Website: numasolution.com
• Phone: +60 3 22842500, +60 3 22843500

Now that CMOs have a seat at the revenue table, there is also pressure to prove ROI. Since the only true measure of ROI is sales, it’s imperative that the marketing and sales leaders are aligned around key objectives and goals to truly prove their contributions to the bottom line. Here Rishi Dave, CMO at Dun & Bradstreet, talks Finance Monthly through the matter.

While sales and marketing teams have made great strides in recent years to better align their outreach to customers, there is still a huge disconnect between the teams and, more importantly, between sales and marketing and the customer. Our recent study showed that, despite increases in new technologies and a proliferation of data and insights, 57% of marketers still find their biggest challenge to be identifying their target customer and the average sales person spends over two hours researching a prospect before making contact. Why are those numbers not improving in lock step with the growth of sales and marketing enablement technologies?

One reason could be the lack of alignment between the sales and marketing departments. And I don’t just mean the age-old disagreement of what’s a good lead and what is considered an opportunity. While those things are important, businesses in this digital world really have to consider aligning around the most foundational element the companies have – and that’s data.

Especially in an environment like Fintech, where we’re dealing with a vast, untapped or underserved community of small businesses, it’s crucial that marketing and sales are aligned on the definition of the B2B prospect – who are our best customers, and where will we find more of them. It’s not just a lead list of businesses and locations: it’s crucial to understand the key factors that will drive a positive sales and marketing engagement, and increase the chance of sales conversion. Factors such as:

• Target profile (age, size, line of business);
• Risk / credit worthiness;
• Changes in behavior (growth, decline, event triggers, etc.);
• Business family relationships.

In the best of circumstances, using analytics, existing customer profiles based on known behaviour, and unknown behaviour from alternative data sources, all brought together to the business entity level, can be used to create advanced marketing models that will target best prospects with precision.

Businesses can also ensure alignment by implementing a master data strategy across the organisation. This may sound daunting, but all it really means is making sure the data you have is structured, cleansed and connected across the company so that insights can be surfaced to the right people at the right time in order to make better business decisions. And, you can start easily by cleaning one app, like CRM, and growing from there.

With a connected view of all customers and prospects, sales and marketing teams are able to make better holistic decisions about each account- decisions which can lead to revenue growth – the ultimate proof of ROI.

By Christopher Hillman, Principal Data Scientist at Think Big Analytics, a Teradata Company

Insurance fraud is a growing problem which many insurers have begun to dedicate new departments and whopping budgets to try and tackle. Huge amounts of time and effort is now spent detecting fraud before paying claims to avoid the complexity and expense of recovering a loss – insurance companies certainly don’t want to pay out claims only then to realise they are fake.

Previously, this process involved manually and laboriously going through masses of individual claims while looking out for suspicious activity, creating a large drain on time, revenue and resources. Now, much of that backend research is being completed faster utilising data and analytics, thereby improving the productivity and efficiency of processes while keeping costs down. Despite this, a significant amount of data that might be meaningful never gets analysed and often, advanced analysts still need to be brought in to uncover meaning from results.

Fraud Invaders: a business case

Imagine being able to cut directly to the chase, removing the human effort needed to tackle huge numbers of worksheets to view potentially fraudulent activity. With advanced analytics and visualisation techniques, this is now possible. To demonstrate, let’s look at a business case called Fraud Invaders.

This case aimed to solve an insurer’s crucial business challenge by discovering a new way to focus on a tighter subset of cases to drive fraud investigation efficiency. To begin, claims documents that had been filled out and submitted by the insurer’s customers were collected, some of which were known to be fraudulent. These known cases of fraud were flagged and put through text mining to extract anything that was a clear identifier such as a bank account, email address or phone number. Following this process, analytics were used to uncover correlations between claims.

With this output, a data visualisation (or network graph) was put together. The resulting image, like the one included below, was made up of dots which represent individual claims, with lines which draw data connections between two or more claim documents. An example of a fraud indicator can be monthly insurance payments from the same bank account: chances are the separate claims belong to the same person or are three different people working together to commit fraud.

Not just a pretty picture: how it works

There’s more to see than initially (and appealingly) meets the eye. The dot clusters visible in the image show us who the “fraud invaders” are. The larger and more apparently connected the cluster, the greater the likelihood of fraudulent activity: this ability to gauge the potential for fraud based on the size of dots and amount of connections can be carried out with the need for little more than a quick look.

Using graphs like these as a foundation, claims teams can identify likely suspects and focus their investigations on these groups. Although not all suspects pulled out will turn out to be fraudsters, far less time, revenue and resources will have been required for this process in comparison to traditional, manual methods. In addition, incidents that may have previously slipped through the net may now be uncovered.

Uncapped opportunity: lessons from Fraud Invaders

In addition to helping insurers to identify fraudulent activity, advanced analytics and visualisation can also reveal networks of people and strong influencers who can assist businesses in attracting new customers, or cause them to lose them. This branch of data science, known as “Social Network Analysis” (not to be confused with Social Media) is a powerful technique that requires true multi-genre analytics. A variety of individual techniques are required to produce a model of a customers’ social network including text mining, fuzzy matching, time series processing and graph analytics. By traversing a persons’ network graph, claim teams can see who they are connected with and who they are influenced by when making decisions such as a purchase or switching services.

Overall, regardless of the desired outcome, Fraud Invaders offers a good lesson to businesses in how to achieve what they want: begin with a solution – rather than just a problem – in mind.

Website: http://www.teradata.com/