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The Business Impact of Predictive Analytics for Financial Institutions

The global financial turmoil coupled with ever-increasing customer expectations and regulatory compliances; are fuelling many transformations in the banking industry. To thrive in today’s challenging business environment, it is imperative for banks and financial institutions to take forward-looking decisions in near real-time. Gone are the days when decisions were taken based on gut feel or hunches, today’s businesses need to inculcate a culture of data-driven decision making. While, most organisations are overwhelmed with data which arises from every interaction, transaction, process, and channel, they also understand the humongous benefits Big Data brings to their organisation.

The real value of big data comes not just by storing or managing it but by analysing it to derive insights and foresights. Predictive analytics presents the potential to enable organisations in transforming data into fore-sight. It can empower businesses to take fact-based decisions, enhance customer engagement, manage risk, curb fraud and attain breakthrough business outcomes.

Predictive analytics is a blend of tools and techniques that enables organisations to identify patterns in data that can be used to make predictions of future outcomes. It not only enables businesses to derive foresights and take forward-looking decisions, but also helps in uncovering and measuring patterns to identify risks and opportunities using transactional, demographic, web-based, historical, textual, and unstructured data. Analysts foresee that, by 2016 nearly seventy percent of high-performing companies will manage their business processes using real-time predictive analytics.

In India, the banking sector was one of the early adopters of analytics. Emerging banks and financial institutions have gradually realized the importance of treating data as an asset. Additionally, many co-operative banks and regional banks are also embarking on analytics journey. In today’s BFSI industry, predictive analytics has its applications across multiple business functions. For instance, customer retention is a major focus for most financial institutions. With predictive analytics, organizations can forecast the potential of a customer to attrite. This gives banks an opportunity to take preventive measures before the attrition happens. Additionally, banks can create a holistic view of the customer and see the relations which he/she or his family has with the bank. This further gives banks the opportunity to up-sell/cross-sell and increase share of wallet.
Furthermore, the BFSI segment faces frauds at different levels. From stolen credit card purchases, money laundering, first-party fraud, malicious transactions, etc., fraud is increasing costs for businesses and consumers alike. Predictive analytics complements an organization’s existing transaction monitoring systems and business rules methodology, by tracking frauds at an early stage and raising red flags. With predictive analytics, organizations can track tomorrow’s fraud today and be proactive in terms of managing potential threats.
Predictive analytics also plays a major role in the Indian Insurance industry, which continues to witness a fierce competition between public and private players. While customers have multiple choices and investment options, the features and benefits of most of the products in this industry is more-or-less the same. As a result consumers choose to opt for plans with lowest premium, at the click of a button. In such cases, it is vital for insurance players to create a differentiated customer experience. However, to achieve this, it is important to identify customer life-stage needs, preferences, investment objectives, service likings, and so on. Today’s insurance organizations are overwhelmed with data, and the volumes are growing rapidly due to telematics, social media, and data from other unstructured sources. It is clear that analytics can act as a key enabler in helping insurers to enhance customer management, mitigate risks, curb fraud and drive business objectives.
One of the key roles of analytics in the insurance industry is in Persistency. Lapse policies are a major concern in a country like India. For instance, if I have my term insurance with an insurer and tomorrow there is an offer from some other insurer on having term insurance combined with some money investment option – the customer tends to switch policy and stops paying premium for the earlier insurance policy, leading to lapse of policy. With predictive analytics, a customer’s life-stage needs can be mapped with the current policy which he/she has chosen. It can also help with coming-up best suited offers that a customer would be likely to subscribe to.
Additionally, predictive analytics helps banks and insurers to create propensity models and win-back strategies to reactivate dormant accounts and lapsed policies. Inputs such as customer life-stage needs, payment mode, product purchased, family details, etc., can be used to prevent attrition and at the same time create up-sell and cross-sell models, that addresses best an individual’s needs.
As technology continues to be viewed as a value creator and a differentiator, technologies such as predictive analytics will always assume high priority for BFSI organizations. Data is continuously flowing from all sides and will further increase with advancements in mobile and social technology. It is important for banks and financial institutions to leverage data as an asset and empower non-technical business users and decision makers to explore their data and discover new business possibilities.

Mr. Sudipta K. Sen, the author is a Regional Director- South East Asia, Vice Chairman and a Board Member of SAS India. 

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