Impact on employment

More than a third of CFOs (38%) expect big data to have a significant impact within the financial sector, particularly on aspects such as job opportunities, with 36% of CFOs seeing big data as a threat to employment. Trends such as robotisation and Artificial Intelligence (AI) are also on the radar of financial directors, with 42% of CFOs expecting AI to have a major impact on employment opportunities and 30% of CFOs seeing robotisation as the biggest threat to jobs.

Marieke Saeij, CEO, Onguard commented: “I’m not surprised that CFOs expect to be completely dependent on big data within such a short timeframe. Big data can help them, as well as finance professionals within their organisations, with the execution of their work. Finance professionals have a great deal of information from both internal and external sources that is of added value for both the performance of the organisation and customer service. The more information that is available about the market and customers, the better finance professionals can advise customers. Thanks to big data, risks can be assessed more accurately and it is also possible to predict in real-time whether and when customers will start paying so as an organisation, you can properly anticipate this. This development will require finance professionals to develop new skills, such as greater analytical capacity, as a necessity.”

Over a third of CFOs see big data as a threat to employment.

About Onguard:

Over the past 25 years, Onguard has grown from a specialist in credit management software to a market leader in innovative solutions in the field of order to cash. The integrated platform ensures that all processes in the order-to-cash chain are optimally linked and that critical data can be shared. Intelligent tools which interface seamlessly combine to provide an overview and control of the payment process and help build lasting customer relationships. Users in over 50 countries worldwide work with the Onguard platform on a daily basis to achieve successful management and tangible results in Order to Cash and Credit Management.

Read more at http://onguard.com/

When you think about it, banking customers are leaving a trail of data when they conduct financial transactions – deposit activity, recurring payments, purchasing behaviours, borrowing activities and even when they just shop for financial services. All customer interactions – whether it is a point of sale, a tap on the screen, or a keystroke – generate insights on purchasing behaviour, clicks, searches, likes, posts and other valuable information.

Data usage has made an important difference in the changing landscape within financial services and the emergence of FinTech companies. Here in the UK, regulatory changes like PSD2 have created a new era of Open Banking where bank customer data will begin to flow amongst financial services providers. With this, the operating model for the traditional financial services companies is changing.

There are new entrant FinTech companies which have shown the ability to access and make sense of data in new and creative ways. Some of these start-ups are giving incumbents a run for their money not because they’re generating or accessing more data, but because they’re looking at it differently and using it in new ways. When FinTech companies get clarity about the use of data, make sense of it, organise and cleanse it, combine traditional and non-traditional sources, they can out-manoeuvre and out-innovate the incumbents.

There are three Vs which are fundamental to the management of data: volume, variety, and velocity.

There are three Vs which are fundamental to the management of data: volume, variety, and velocity. Given the increasingly competitive environment, evolving customer expectations, and regulatory constraints, financial services providers are seeking new ways to leverage data and technology to gain efficiency and a competitive advantage. The adoption of Big Data and new data management strategies is redefining the competitive landscape of financial services and companies that don’t have a strategy run the risk of losing market share.

To address this situation, financial services companies are investing in new and modern data management strategies that address both enterprise data and their Big Data assets. This new data environment must act at the speed of business, offering real-time insights that are created using massive volumes of data. New data-driven innovations include analytical tools such as machine learning and predictive analytics. These capabilities connect and leverage data across their entire enterprise and outside partners.

With all the changes taking place, there are many challenges and opportunities. Based on our experience working with many of the largest global financial services companies, we have observed a lot of focus and investment in these three following areas:

1. Creation of a Unified Financial Services Data Model.

This represents a standardised, multipurpose data model that creates a single, consistent view of the customer. This modern data environment is a business-driven data model that should serve all analytical requirements. It should also support all business domains such as marketing, risk management, product, customer experience, compliance, regulatory reporting, finance, and other functional areas.

It is critical that this environment is extensible and supports ongoing change. The activation of data that is stored must provide simple access for analytical applications as marketing, customer experience management, risk and other functions must respond in a real-time manner to create the desired customer experience or prevent fraud from occurring.

There are many other capabilities that can be delivered from this Unified Data environment. It is a foundational capability to address the rapid explosion of data, channels, devices, and applications.

2. While data collection is important, collecting more data is not always the answer. Ingesting the best sources and continuously testing them for accuracy and predictive capabilities is critical. New alternative sources of data are being created every day. While some of these sources can create some unique value, other sources may only add complexity to data management and cost without the desired return.

Deep mining of data can help predict needs and enable a much-improved customer experience. Improving the quality and accuracy of data that is collected, stored in the cloud, processed and analysed by artificial intelligence and deployed is important when creating new targeted offers and enhancing a customer experience.

Diligence in the areas of consumer privacy and security is and will continue to be paramount.

3. Diligence in the areas of consumer privacy and security is and will continue to be paramount. Consumer understanding of how their data is used often lags behind the pace of innovation, inspiring new demands from government agencies and consumer advocacy groups around the world. These factors compound the liability every financial services company faces when managing and activating consumer data.

Data security and privacy is an important issue and historically has been a strong point of differentiation for financial services companies, especially in light of the continued discussion around how Facebook and other social media companies manage data. There is and will always be an expectation that financial services companies remain a trusted guardian of data.

As financial services leaders realise that more trusted, connected and intelligent data contributes to their competitive position and survival, they now see data as an essential asset. This asset also requires investment to unlock value. Data should not be looked at as a driver of costs, but an important asset that will pay off handsomely for tomorrow’s financial services leaders.

About Scott Woepke

Scott Woepke is Head of Financial Services Strategy at global data, marketing and technology company Acxiom, where he leads a global team. He has over 30 years of hands-on experience in many facets of marketing, distribution, product, and technology strategy in the financial services and FinTech industries. His work includes working with many of the world’s largest financial services companies across retail/consumer banking, credit cards, investment services and payments.

 Website: https://www.acxiom.co.uk/

Protagonist of this week's news, Alexander Nix is the executive at the centre of the Cambridge Analytica and Facebook controversy surrounding political campaign influence, sly data based marketing and supposed behind-our-backs data harvesting through everyone's favourite social media platform.

In this video CEO Today delves in to the life of Alexander Nix, a very private individual, listing some hobbies, interests and much of what he's been up to to get where he is today.

In Latin America, the Big Data and Analytics (BDA) market is gaining pace and undergoing an intense evolution. Innovative business models such as Internet of Things and cloud computing are transforming the market and creating new ways to collect data and improve data storage processes. In addition, companies in the region are becoming more familiar with the concepts and benefits of adopting and implementing BDA solutions.

"Exponential data growth fuelled by connected devices has compelled organizations to revisit their ability to use Big Data to make more intelligent, real-time decisions. Considering the hyper-competitive business environment, this critical need has given rise to a new breed of analytics solutions focused on prediction, data visualization, and dynamic decision making," said Frost & Sullivan Digital Transformation Consulting and Research Director Renato Pasquini. "Technology providers such as IBM, Oracle, SAP, SAS and Teradata are market leaders and have focused on providing solutions for real-time analysis in the Latin American BDA market."

Latin America Big Data and Analytics Market, Forecast to 2022, is part of Frost & Sullivan's IT Services & Applications Growth Partnership Subscription. The total BDA market in Latin America earned $2.48 billion in revenues in 2016. Led by Brazil and Mexico, and driven by digital transformation, the market is expected to reach $7.41 billion in 2022.

Integrating a secure BDA solution into existing legacy infrastructure remains a key challenge, along with acquiring and sourcing talent for analytical and technical skills. Nevertheless, companies are realizing that they need to invest in BDA solutions and find innovative solutions to overcome these challenges in order to remain competitive in a dynamically evolving ecosystem.

Other developments include:

• The Latin America market represents approximately 5.1% of the global BDA market.
• In 2016, the Brazilian market represented 46.8% of the BDA market in Latin America. It is the most mature and generates revenues of $1.16 billion.
• Financial services, retail and telecommunications are key verticals making significant investments in BDA.
• Improving customers' experience is the main reason for investing in BDA solutions.

"Hadoop is becoming the standard for the majority of Big Data projects. This is due to its disruptive characteristics such as open source, free, scalable, low cost and fault tolerance. Once cloud and Hadoop are compatible it would make sense to run them together as they both focus on reliability and scalability at a reasonable price point, which is essential for BDA solutions," noted Pasquini.

(Source: Frost & Sullivan)

Data should be one of your strongest assets, not a confusing uncertainty or a burden to work with. Alastair Luff of global information services group Experian here talks about how you can make the most of the data you gather and use it for e key decision making in your operations.

Big Data has become a buzzword in the Financial Services industry. Put simply, it’s about businesses having an amount of data so large, it becomes difficult to digest and define a clear strategy.

Information is created every second of the day, and its complexity is advancing as new data comes onto the scene. The volume of data is growing significantly, presenting a notable challenge to businesses. On its own, data isn’t valuable – it’s the business insights it provides which makes it a vital asset. The more information, the greater the insight, and the bigger the opportunity to drive optimum outcomes.

Data – a confusion or a complement?

Data can seem daunting. It needs to be controlled, understood and used to avoid hindering compliance, and to create real value. It can also confuse the customer – with less than 8% understanding how their data is being used within organisations.

But it can also complement. Organisations are not only faced with external data sources, but also first party data generated internally. But two data streams doesn’t result in a complete customer profile, and in some situations, information captured over an extended period of time may become outdated. Overlaying current and validated data, such as credit bureau data, can add a layer of insight that fills gaps and helps complete a fuller picture.

The more comprehensive view available, the better lenders can tailor credit risk policies to ensure financially inclusive lending strategies that consider all relevant data assets, e.g. within credit scoring.

Scoring with the customer

Credit scoring is nothing new, but it’s not just about banks and lenders. Industries outside of finance are beginning to recognise its benefits and scoring is offering enhanced outcomes for customer engagement and enhanced credit risk provisioning. In Africa, for example, data from mobile phone usage is helping with credit scoring where no financial services data exists, giving more people access to credit.

While scoring itself is well established, the process behind it has evolved. Organisations, lenders especially, are approaching scoring differently, considering individual risk strategies, profiling and in some instances different data assets. All of these factors, whether standard or bespoke, can provide an automated risk assessment that identifies the credit strategy of an individual.

Simplifying complex information

The ability to make responsible lending decisions comes down to how well information is interpreted. This is where scorecards come in to their own. They can help rationalise complex insights and automate decision making. Businesses who overlay internal insight into scoring, with enriched external insight achieve a more comprehensive view of each customer’s credit history.

In an era confused by a mass of information, a more demanding customer and pressure on minimising loss, businesses need to understand the value and opportunity – but balance both. This extends beyond scoring as an action, and therefore it would be prudent businesses automate this area – using available insight to free up resource to support developments across other business areas which aren’t so easily resolved.

Using comprehensive scoring can provide advanced data feeds that contain varying benefits for the organisation, for example:

1. Understanding affordability. What does the future financial health of an individual look like? Are they likely to experience problems?
2. Geographical insight. Some people have little or no bureau data. Using geographical analysis can provide a view of how the region and area is trending to support any credit review.
3. Considering circumstance. Data for those with limited data, for example people living at home with their parents, can have their profile enhanced by overlaying relevant data. In addition lenders can consider the financial status of an individual, or an associate who they are linked to financially. This can provide a rounded view of any associations and identify any causes for concern.
4. Ensuring the person is genuine. Fraud is on the rise and using data to assess and identify the genuine intent of a person can be critical to losses and also protect customers.
5. Understanding how a person behaves. Behavioural data can provide rich insight into a person’s financial behaviours. From cash advances on credit, to limit vs. spend assessments. This can be particularly helpful in understanding a person’s financial trends and providing a prediction into their probable future trends.

Differing and advanced data assets can be used to on-board, and when a customer is on-boarded. It can be particularly useful during the lifetime of a loan in order to understand better any potential alignment to a business’s growth strategy.

In a world of Big Data, organisations have the opportunity to translate information into a currency. Understanding what insight it can bring, embedding it within credit risk and scoring policies can ensure accurate assessments and appropriate lending. Businesses just need to understand what data provides what – and why.