Enterprise & Market Resilience Middle East Forum (Session 5): Unlocking Value from Data - A Financial Services Perspective

The Tipping Point – Data & Analytics

The moderator set the context by highlighting the changing data landscape for financial services institutions in terms of enormous availability of both structured and unstructured data, phenomenal advances in computing capabilities, and access to powerful and sophisticated data analytics tools. In addition, as has been the case over the last decade, data scientists continue to make tremendous advances in the fields of machine learning, artificial intelligence and deep learning. However, not all organizations have been able to embrace the opportunities by data science and data analytics – and a major factor influencing the rate of adoption and operationalization of data initiatives is the board-level perspective or the ‘tone-at-the-top’.

Tone-at-the-top – The Key to Successful Adoption

Awareness regarding the role of data as a critical driver of business objectives has increased in recent years. Historically, the usage of data was limited to the domains of risk and regulatory compliance, with traditional statistical analysis used for limited applications such as scorecards. With time, however, the scope and dimensions of usage has widened to a number of areas in a financial services institution across the customer, product, services and channel analytics.

Data is being increasingly seen as an asset and a lever to build competitive advantage by financial services institutions. Further contributing to this trend is the increased regulatory awareness and push towards leveraging both internal and external data, and advanced analytics. For example, the Central Bank of Kuwait recently requested banks within its jurisdiction to share their data strategy and how they are using data and digital levers to enhance their operating effectiveness and customer service objectives.

A Global Health Crisis driving a sense of urgency

The COVID-19 crisis and its resulting impact on customer behavior, coupled with the increasing need to make sense of data and take swift decisions has the potential to quickly turn advanced data analytics from a competitive advantage to a must-have capability for survival. The concepts of remote working, a need for maintaining minimum levels of services, the need to anticipate customer priorities and hence streamline bandwidth of employees, service channels and technical infrastructure are a few of the operational challenges where advanced analytics can provide useful insights and solutions.

The factors driving the sense of urgency for adoption of data and analytics by financial services institutions are three-fold –competition, regulatory awareness, and changing customer behavior exacerbated by the COVID-19 crisis. With regulators nowadays operating at levels wherein they themselves consume or leverage a huge volume of data towards monitoring and decision-making, it is natural that they will expect the institutions to also operate at same level. The need for a speedy response to regulators will further warrant that institutions have data and analytics capabilities implemented in a robust and reliable manner.

Where to focus

Given the rapidly changing business landscape, boards and senior management of financial services institutions are prioritizing certain business and operational areas for usage and adoption of data and analytics. These include several dimensions of customer lifecycle i.e. segmentation, selection, origination, and retention; of the product lifecycle i.e. development and feature enhancement; and of services selection such as loyalty programmes and bespoke credit card offerings. Overall, there is expected to be a tremendous potential and hence focus of usage especially in the retail segment on the banking side, and towards derivation of customer insights and behaviors in the high-focus areas of the insurance sector, such as medical and motor insurance, especially with the uptake of adoption of telematics in the region.

In terms of internal usage of data and analytics in light of the COVID-19 crisis, applications span areas such as analyzing employee work times to maintain requisite service levels and gauge effectiveness of the work-from-home paradigm, branch rationalization, monitoring cyber risk to ensure operational resilience, cost optimization and fraud monitoring. Specifically on the insurance side, fraud detection and abuse management is an area of application that is seeing a rapid adoption of data analytics.

When asked about senior management’s propensity to invest in data projects, attendees identified strategic investments as a top priority, followed by projects where the importance is clearly understood but the budgets are constrained; however projects with immediate / short term returns and those with limited buy ins are likely to be deprioritized in the current scenario.

Data Monetization –To be or not to be

Traditionally, it has been a challenge for financial services institutions, especially banks, to monetize available data due to regulatory apprehensions around sharing of customer data. The sharing of data in this context has typically been one-sided so far with banks engaging with external third-party data providers to supplement customer data available within the institution, for example, telecom firms for customer service and product development. Furthermore, financial services institutions themselves are cognizant of the need to be extremely cautious when using customer data since they are seen as trusted service providers.

However, there are some early and encouraging signs of a shift in perception regarding leveraging and sharing of data to enhance its applicability and usage to derive useful customer insights. An example is the recent PSD2 directive in Europe which not only allows but also requires banks to share their data. A key success factor towards data monetization going ahead will be financial services institutions’ ability to share data while maintaining customer confidentiality and perception as a trusted service provider. Some action is already being seen in this direction by banks choosing to acquire stakes in emerging fin-tech start-ups with the aim of obtaining a controlled approach towards analysis of their internal data.

Who should drive the Data Agenda within the Organization?

The panel reflected that the ownership of data analytics within a financial services institution is best left to the team that ultimately derives benefits from its insights. These teams may include business as well as enterprise functions such as finance, risk, etc.

However, in addition to data ownership, defining a robust data governance framework and identification of data stewards and data champions will be critical towards successfully driving the data agenda within the organization. This involves having well-defined rules of ownership, roles and responsibilities and KPIs in place as part of the data governance framework.

In a poll about availability of good, clean data to drive business decisions, attendees identified the most common state of available data as data being partly digital; however the extremes of largely manual availability of data and fully digitalized, robust data were viewed to be equally unlikely.

The Way Forward – Emerging Tools for Evolving Business Needs

The panelists highlighted the importance of continuing to leverage the latest advances in machine learning, artificial intelligence and deep learning. Financial services institutions have so far been able to use structured data to their advantage on the risk side through traditional statistical analysis, and the focus should be to extend the same degree of success to analysis of unstructured data through advanced analytical methods, towards both business and risk.

In the insurance sector, data and analytics will continue to see application in the consumer side of the business given the huge transaction volumes, especially in the medical, life and motor insurance spaces. This has led to an increasing adoption of technologies such as telematics (motor insurance), deep learning algorithms for automating OPD claims adjudication (medical insurance), especially in the developed markets.

While the advantages of leveraging the latest tools for data analytics may be obvious and intriguing, the panelists cautioned against excessive focus on procuring the latest ‘shiny toys’ as opposed to leveraging the existing investments and solving foundational data issues. Also, getting the job done is more important than how we do it – hence leading with a technology solution is often not the right approach.  

When asked to opine on where their institutions are placed in their data and analytics journey, most attendees identified that it is typical that different departments within their organizations, such as finance, risk and business manage and govern data in their own silos.