RFP Blogs

The financial services sector is typically expert at churning vast amounts of data, gathered over decades of customer interactions and transactions. Some financial institutions (FIs) might be said to have perfected the art of data analytics, but there is always more to learn about how ‘big data’ can be harnessed by FIs.

Analytics experts, whether at FIs or not, juggle unimaginable amounts of data every day to extract insights to enable decisions that lead to better understanding of customer needs, designing exacting services and products and subsequently running targeted marketing campaigns. Moreover, analysing big data properly helps predict future customer needs and can even give a good sense of potential customer attrition. All of these quite naturally help accelerate revenues, increase market share, reduce costs and minimise fraud, which is why big data is important.

Customer data is analysed to answer certain key requirements. For example: a financial services firm may categorise data using the following approach (inspired by the Johari Window model):

1) Known to bank: This would include the client’s demographic details, balance maintained in account, transactions made, products held, etc.

2) Known to customer: wealth information, cash flow status, etc.

3) Known to bank but not to customer: customer profitability, internal credit ratings, etc.

4) Unknown to bank and customer: customer’s future financial potential, job status, etc.

For the analytics team in a financial services firm, success lies in knowing more about the data known to the customer (see pt. 2 above) either through constant interaction or via pattern analysis / data mining / analytics using the existing data.

All this is possible using internal data gathered over the years and external data sourced from social media, industry research, etc. This practice has been prevalent for decades now, but the social media aspect is new and has spawned the ‘big data’ buzz word. Some banks have begun storing all relevant financial / demographic information for future use. There are also several banks that gather substantial customer data initially but do not completely transfer it to their systems, for various reasons. A significant portion of the information provided by customers often gets left behind in application forms and correspondence.

A recent survey on data management in banks and investment services firms by Gartner underscores the vast potential of big data analytics within the financial services sector. Astonishingly, only a third of the financial sector rated quality of data (for supporting business intelligence and management decision making) high on priority, thus suggesting room for IT investments.

In addition, there is an interesting shift in the way regulators are monitoring the operations of financial services firms and insisting upon transparency. For instance, India's Central Bank, the Reserve Bank of India (RBI) has directed all banks to standardise their regulatory reporting by following an automated data flow (ADF) approach to ensure 100% accuracy and zero human intervention in every stage of reporting: right from data extraction from source systems till the actual submission of returns (reports) on to RBI's Central Data Repository. Firms that cannot utilise complete information (probably due to storage challenges) and firms that believed reporting didn’t really require management attention, are now warming up to the new big data reality.

The financial services sector is also grappling with a sudden spike in new data avatars and sources, in addition to exploding volumes of multi-structured data that cannot be easily (and cost-effectively) stored in conventional systems. Very large and complex data stores actually hinder effective data processing and analysis using orthodox tools.

Regulations 

Apart from enhancing growth and bettering processes, financial services firms now have a new regulation-driven incentive to apply insightful data analytics. Never before has any sector experienced a phase that is as trying as now. Regulators are relentlessly demanding more transparency and better risk management, even suggesting that if using better technology is the way forward, then so be it. New regulatory demands from the RBI ADF, to the Basel II and III capital adequacy rules, IFRS, and so on are all ‘front of mind’.

This requires a new mindset and a whole new approach towards information management. Firms are gradually realising that by migrating to the world of big data, almost every data related requirement can actually be achieved from a single platform alone. Financial services firms that have adopted this option now not only have more, better and vital information (that existed earlier in forms filled out by customers) readily available, but their total cost of ownership (TCO) for data storage has dramatically declined as well, making analysis and volume storage easier.

Actionable insights (that may have far-reaching impact) born from analysing information justifies all the effort and time to store the vast amounts of necessary data. CEB TowerGroup's recent report on the capital markets' top 10 tech initiatives, forecasts 2012 to be the year when firms will begin to employ big data techniques to address challenges in risk management, regulatory compliance, and portfolio analytics.

Big data analytics in fact goes beyond enabling decision-making. The scope is getting wider and deeper, something which financial services firms have begun experiencing. What used to be a tool for risk management is now touching exiting new horizons. Financial services companies now better understand the importance of effective collaboration between their IT and analytics teams to establish efficient big data platforms as an ‘edge to business’, whether they are for use on regulatory reporting or customer retention / winning. The era of big data is here and needs to be harnessed.