This week Finance Monthly hears from Mohit Manchanda, Head of F&A and Consulting EXL Service UK/Europe at EXL, on the ever-evolving DNA of a CFO.

Business leaders have to stay relevant and ahead of the curve and adapt to the constantly evolving world of finance. This development has become ever apparent for the Chief Financial Officer (CFO) whose role now includes, strategies, operations, communication, and leadership as well as building knowledge surrounding the impact of emerging technologies within the finance sector.

Business outcomes

Advances in data software and automation are opening up avenues for businesses to generate valuable insights that can lead to major productivity improvements. Within the finance and accounting areas, technology is becoming a catalyst for change, driving innovation and providing operational efficiency in business-critical functions.[1] It is essential for CFOs to rethink how to utilise this opportunity to streamline their processes for efficiency, compliance and risk management.

CFOs have many objectives to commit to and by using cutting-edge solutions to enhance the transparency and accuracy of financial data, they can better manage the financial management process. Using automation within finance helps to free up high-value tasks and alleviates the pressure on the CFO to perform traditional activities such as, transaction processing, auditing and compliance.

Human X Machine

It is becoming more and more evident that the CFO will be looked up to, to drive the utilisation of new technologies, however they should try not to get ahead of themselves and forget about the day to day business. Becoming too attached to the hype surrounding Automation and Analytics can put other business objectives on the back burner. For example, managing costs and coming up with new ways to generate profit are tasks that require the CFO to use their own industry knowledge rather than relying on data or analytics.

New technologies can speed up processes and lessen tasks for CFOs; it is important for them to make choices and identify processes where AI, Automation and machine Learning adds value. An investment in one area of a business can create savings in another. In most companies, a high percentage of staff still perform tasks that can be automated through Machine Learning, and these tasks can be performed exponentially faster if self-learning algorithms are applied.

Given the pace of technological change, CFOs should carefully evaluate their point of entry and roll out multiple pilots or proofs of concept (PoC) to test and secure validation before deploying these new technologies.

New technologies can speed up processes and lessen tasks for CFOs; it is important for them to make choices and identify processes where AI, Automation and machine Learning adds value.

Introducing innovative technologies within the finance sector does aid in mitigating lesser tasks for the CFO, however it is not only the technology alone that enables a more streamlined work process. By combining talent, skill set and technology together creates a unified approach, resulting in major improvements throughout the business. For CFOs it means that they can move away from everyday traditional accounting tasks, therefore freeing up time to use their industry knowledge to focus on new business opportunities and provide strategic guidance.

Data & Domain

Organisations regardless of their size will collect large masses of data of which most will never be utilised. It is important for CFOs to understand which data sets are of value and which ones aren’t. Some may be needed for regulatory purposes and others for commercial predictions and products, however by disregarding the sets that are not of value helps to create a more streamlined result.

Starting to experiment with data will help identify potential risks before they are put into production. Machine Learning is all about data experimentation, hypothesis testing, fine tuning data models and Automation. Bringing data, technology and talent together in the form of ideation forums, innovation labs and skunk work projects allows discrete data to be tested for the first time. By bringing in Machine Learning, it can identify hidden patterns that could potentially harm the production process.

In order to drive the business forward, CFOs can translate data and combine it with industry knowledge. The data helps to provide insight within the industry which then contextualises their business decisions. Using data driven decisions CFOs can be confident in their choices within the organisation and use it to back up or prove their conclusions.

Putting data under the business lens enables a CFO to understand the repercussions that can occur through the improper use of big data. A business’ reputation is on the line if data violations occur. Not only will this result in legal sanctions, it will limit business operations, which will have a domino effect on resources and a company’s position compared to its competitors.

Therefore, CFOs should review all of the potential consequences before putting their experimented data findings into practice, including any legal, financial, and brand implications. This is where industry knowledge comes into play, using an expert committee on business data to inspect algorithms for unintentional consequences, results in less risk than normally associated with Machine Learning.

For CFOs to thrive in the digital age, it is essential for them to have a unified approach combining industry knowledge, data, technology and talent.

For CFOs to thrive in the digital age, it is essential for them to have a unified approach combining industry knowledge, data, technology and talent. By employing new technologies, data, talent and knowledge as one package, CFOs can add continuous learning opportunities for critical talent pools, and assist in the overall improvement of productivity within the business.

[1] https://www.business2community.com/big-data/17-statistics-showcasing-role-data-digital-transformation-01970571

2018 has been the year that the financial services industry welcomed machine learning (ML) and artificial intelligence (AI) with open arms. However, there is much hearsay on the topic of machine learning, so what should anyone believe? Let’s start with three big myths, as explained by Dave Webber, director of concept management, data strategy and innovation, TransUnion.

While reports last year found that the financial industry was seriously lagging in its adoption – only 37% of organisations said they expected to use AI functionality within the next 18 months – there has been a step-change this year. A study by Adobe and Econsultancy found that the majority (61%) are either already using AI, or plan to adopt the technology within the next 12 months.

Whilst these numbers are positive, it still highlights how over a third of financial services businesses still aren’t even considering using ML or AI. This needs to change.

Both AI and ML have the ability to completely alter and change the way we do business, streamlining processes, reducing costs and improving efficiency. You only need to look at the benefits other sectors, which adopted the technology earlier, are experiencing right now to be convinced. For example, retail businesses are using ML to provide convenient, responsive and personalised services for customers based on their online browsing behaviour, while in healthcare, computer-assisted diagnoses are being used to uncover diseases quicker and more accurately than the human eye.

The benefits of AI and ML are numerous, but in the financial industry, there are still a number of common misconceptions and myths that are slowing down adoption of the technology. It’s time we dispelled them.

1. Machine learning is too risky for financial services

Trust takes on an entirely different and elevated meaning in the finance sector compared to an industry like retail, in which a trial mentality is more acceptable. Fraud protection specialists, for example, may therefore place more trust in traditional systems that they have always used successfully to flag fraudulent applications.

But as fraudsters grow ever more sophisticated with their techniques, a greater array of data and tools are required to detect suspicious or criminal activity, and the demands on the human workforce to spot things will be too high. Through ML, we’re instead able to pull together a vast amount of contextual data to determine whether an application is legitimate.

Over time, automated systems can become more adept at spotting irregular data, resulting in more efficient fraud detection. Believing ML is too much of a risk in financial services is risky in itself. There is simply too much at stake when it comes to detecting and fighting fraud. By letting ML make decisions based on accurate and robust data, you can spend more time and attention on what the parameters of these decisions should be.

2. Machine learning is not ready to make better decisions

Financial institutions are understandably hesitant to adopt unproven technology, but in actual fact, we’ve been using ML and AI-based technology in our daily lives for years. Whether it be Amazon’s product recommendations, or asking Google or Siri for the quickest route home, we already trust ML implicitly – often without even realising.

The benefits of this realisation are also not limited to just fighting fraud; ML can also be a great asset in credit risk and affordability modelling. Through analysing huge amounts of existing data, the technology can help lenders back up their decisions to both consumers and regulators. It can also be used to flag applications where a customer is likely to default, as sample sets can be contrasted with actual decisions of the time to modify a business’s risk profile.

Far from exploring uncharted territory, ML focuses on using data to allow both man and machine to make better decisions together. Here at TransUnion, we conducted our own year-long machine learning trial which highlighted the possible benefits in the credit, fraud and insurance industries. In one case, the level of default in a portfolio of 60,000 credit cards was significantly reduced, whilst overall bad debt dropped by 10%.

3. Machine learning is difficult and time-consuming to implement

Owing to its growing status as the next generation of essential predictive tools, it is justifiable to view ML as an arcane technology that only a select few people can understand. But in reality it is relatively simple to adopt, and is primarily focused on supplementing traditional methods by working alongside existing systems.

As its name may suggest, ML is a largely autonomous process; it develops its algorithms over time as more and more data is used. This results in the quick and easy creation of more bespoke, dynamic and constantly developing models.

Clearly, far from being a time-consuming burden, the way ML can assist and supplement existing jobs will lead to dramatic increases in productivity and efficiency.

2018 has been a landmark year so far for the technology, but for the adoption rate to keep climbing, we need to bust these myths. Society already trusts ML implicitly, and the firms that are able to accept this fact and embrace it won’t just have the potential for substantial increases in productivity and efficiency, but they will ensure that they remain relevant in the future.

Far from taking human jobs in future, Artificial Intelligence (AI) and Machine Learning (ML) technologies are going to free up finance professionals from spending too much time on monotonous tasks and allow them to focus on more strategic tasks of higher value to the business. Does this mean that finance roles will mostly be driven by robots? Below Tim Wakeford, VP of financials product strategy EMEA at Workday, discusses with Finance Monthly.

A recent EY study revealed that the majority (65%) of finance leaders said that having standardised and automated processes—with agility and quality built into those processes—was a significant priority when it came to investing in emerging AI and other technologies. And, following on from this, 67% of finance leaders said that improving the relationship between finance and the wider business strategy was also a key priority.

Again, this is an area where automation and AI technologies are helping free up time for finance to spend more time working with other teams within the business. This enables them to figure out where to go next as opposed to looking backwards and dealing with unproductive and time-consuming legacy finance systems.

Freeing up talent to focus on high-value tasks

Freeing people up from repetitive jobs to enable them to focus on high-value tasks is the opposite of the oft-cited “robots putting people out of work” narrative.

Indeed, automation is a huge opportunity to reduce the unnecessary burden and pressure that’s put on finance professionals, particularly around traditional tasks such as transaction processing, and audit and compliance.

The adoption of AI applications within finance enables forward-thinking executives to move info far more strategic business advisory roles. This means that they can focus less on number crunching and more on financial analytics and forecasting, strategic risk and resilience, and compliance and control. This shift to data-driven financial management delivers a much wider benefit across the business.

The Rise of the robots: AI in finance

Computer systems performing tasks that previously required human intelligence is the definition of AI, with experts viewing AI and automation as viable solutions to efficiently deal with compliance and risk challenges across different sectors.

With the rise of the ‘big data’ era comes a parallel growth in the need to analyse data for financial executives to be able to properly manage compliance and risk.

This is another reason why finance teams cannot ignore the opportunities that embracing AI technologies offers them. It allows them to process vast amounts of data faster and easier than large teams of humans can.

Individuals are then able to make better strategic decisions based on the information that AI is able to rapidly extract from what were previously time-consuming and repetitive and monotonous tasks such as transaction processing.

Jobs least likely to go to robots

Forward-thinking and highly-skilled financial executives are happily embracing AI, as they see the clear opportunity it presents to play a more valuable and strategic role within their organisation.

“The challenge for managers will be to identify where automation could transform their organisations, and then figure out where to unlock value, given the cost of replacing human labour with machines and the complexity of adapting business processes to a changed workplace.” This is how writers James Manyika, Michael Chui and Mehdi Miremadi so fittingly describe the process in their book These Are the Jobs Least Likely to Go to Robots.

“Most benefits may come not from reducing labour costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.”

AI and automation in finance has to be about reducing repetitive manual tasks and raising overall productivity through data-driven business strategy. The bottom line is this: any technology that can reduce manual input and the associated human errors for transaction processing and governance, risk, and control (GRC) will free up finance professionals for more strategic work.

Any organisation’s most important asset is its people. And finding out which emergent AI technologies and applications are the best for a business and its people is going to be key for the future of finance.

Giving skilled finance staff the autonomy and opportunity to move into far more strategic data interpretation roles and letting the machines take on the grunt work is a necessary shift in the finance function.

As well as automating a large part of the finance function, AI technology will also help skilled finance executives to make a far more sophisticated analysis of complex data sets and to provide genuinely valuable insight to drive the business forward.

There is very little doubt that the future of finance will be one that embraces technological innovations to improve effectiveness, increase efficiency, and enhance insight.

The AI technology has been picking up steam in the past couple of years. It’s no longer a gimmick or a faraway fiction. Scientists from all around the world are slowly but surely cracking this riddle. Sure, they are still a long journey away from creating a true Artificial Intelligence, but each year we see significant breakthroughs in this field.

Today, you can find some form of AI in many everyday places. For example, Alexa and Siri are world famous AI assistants. They will create appointments, answer your questions, set alarms, shop, and a million other things. Another great example is the Tesla car. Thanks to Tesla’s AI, self-driving cars are no longer a work of fiction.

But what about the poker industry? Surely there must be an AI capable of playing poker at high levels. The answer is yes, there is. This infographic will show you how the poker’s AI developed throughout the history, as well as where it is now. You can find a lot of interesting stats and information in this infographic, but if you are interested in reading more about poker related stuff, visit our website.

Flashback 20 (or so) years to 1996. Kodak, seen at the time as one of the world’s leading technology innovators, was worth $38billion and employed 140,000 people. That’s an average worth of $270,000 per employee.

Skip forward to recent times. YouTube sold for $1.65 billion and employed 65 employees - placing each employee’s value at $25m. Instagram then sold to Facebook for $1b with just 13 employees (each worth a cool $77 million). WhatsApp then blew both out of the water - selling for $19 billion and in the process, if you apply the same formula, making its 55 employees worth a staggering a $345m a head.

Technology has allowed the emergence of a term coined exponential organisations - these are exponentially fast-growing companies that leverage technology. They require less employees but more tech savvy ones. More and more companies are trying to replicate this model - that is: hire less but more tech savvy people - and as this happens, job roles are slowly being replaced by skillsets. Employers require their staff to have an ever-growing number of skills.

A McKinsey report from late 2015 stated that 45 per cent of the activities individuals are currently paid to perform could be automated by adapting currently demonstrated technologies. It’s not just checkout operators or baggage handlers who are being replaced, either. They discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, senior executives, including CEOs, have a significant amount of activity that can be automated.

A World Economic Forum summary about the future of jobs found that by 2020 - just three years from now - a third of desired skill sets of most occupations are not considered crucial to the same jobs today. A direct side-effect of this rapid change in such a short amount of time is a major digital skills shortage crisis, which the UK’s Science and Technology Committee published a report warning of mid last year.

All of this paints a rather grim picture for the amount of jobs available in the future and the number of people with the required skillsets available to do these jobs. But, the good news, according to London’s first monthly growth marketing course provider, Growth Tribe, is that you can future-proof your career and your own skill-set.

Master the fundamentals and you can master the rest. Below are five things you can do right now to get ready for the future - which is already kind of here!

Self-learn

Learning doesn’t stop after you leave university. Some, like billionaire entrepreneur Peter Thiel, are even arguing that it shouldn’t start there in the first place. The Shadbolt Review of Computer Sciences Degree Accreditation and Graduate Employability from last year also found a clear disconnect between what employers need and what universities teach.

But you needn’t panic. It’s never been as easy to take education into your own hands. On and offline courses are readily available and affordable. You can take learning how to work better with technology into your own hands.

Learn the coding basics. Learn about behavioural psychology and automation tools. Play with data and think about how your company could use technology to improve user experience. Stay curious, seek out relevant training and use the resource in your back pocket to upskill on the go.

Start a company

Perhaps one of the greatest ways to learn about business is by starting your own. Investing in and starting your own business forces you to solve problems, grow, learn and adapt - if you want to succeed, that is.

You’ll be future-proofing yourself without even realising as you work to create your own website, social media strategy, app, marketing and sales channels and plans, etc.

You also don’t need a million pounds to start. Noah Everett, Twitpic and Pingly founder said: “Don’t worry about funding if you don’t need it. Today it’s cheaper to start a business than ever.” In the UK, online accounting firm FreeAgent found that the majority of UK freelancers and micro-business owners were self-funding their start-up costs rather than relying on external funding. Almost half (44 per cent) of respondents didn’t require any funding to get their business venture started, while 43 per cent had only used personal savings to do so.

Solve problems

There are little day-to-day issues all around us, every day, that could be made easier with the use of technology. Think about contactless payment, for example. We mightn’t have thought it could get much easier than punching a few numbers in at the till, but hey presto, we use contactless pay for a few a months and all of a sudden we’re at a place where having to key your pin in seems like a bit of a pain.

Apps like Be My Eyes don’t rely on overly sophisticated tech, but they do solve a really simple problem. In this case, it helps visually impaired and blind people around the world. Using the camera functionality on a smartphone and the assistance of an able-sighted volunteer anywhere in the world, visually impaired people can quickly check that they’re choosing the tin of tomatoes as opposed to that of beans, for example. It means they don’t have to wait until an able-sighted friend or family member is available and they can quickly get on with their life. It’s a really simple idea but for those benefitting from the app - it’s a game changer.

Develop a growth mindset

If you have the desire and the mindset, you can learn anything. A “growth mindset”, a term famously coined by psychologist Carol Dweck, refers to a person’s self-belief about their own abilities. Those with a growth mindset believe that their most most basic abilities can be developed with dedication and hard-work.

It’s quite empowering when you think about it - you are no longer bound by preconceived perceptions about your own intellectual abilities!

Famous examples of people with a growth mindset include Richard Branson, Malala Yousafazi, Elon Musk, Brian Balfour… think about any inspirational person that you associate with entrepreneurialism or who in some way is pushing boundaries and punching above their weight. Chances are it’s not because they were born with any special gift or ability more impressive than most of us - it’s because they have a finely tuned growth mindset - they’re willing to try, to fail, to learn and to keep growing.

Create

Finally - start doing. David Arnoux says: your greatest credential in this era is your output of stuff. The skills listed on your CV are just words without proof. There are cheap and easy to use tools which allow you to build, create and showcase. Think of it as a live CV and proof of your awesomeness.

It might not quite be an app (or it might) - the choice is up to you. Use what is available to you to build a website, a blog, a prototype, a simple data model... build stuff and showcase it.

“Traditionally, life has been divided into two main parts: a period of learning, followed by a period of working. Very soon this traditional model will become utterly obsolete, and the only way for humans to stay in the game will be to keep learning throughout their lives and to reinvent themselves repeatedly,” says Yuval Noah Harari.

(Source: Growth Tribe)