Mr. Maninder Singh Grewal is the Chief Data & Analytics Officer at one of the fastest growing digital lending apps in India- mPokket. With more than 16 years of experience across data sciences, predictive analytics, artificial intelligence (AI), machine learning (ML), deep learning and consulting domains, Maninder is building a multi-layered techno-functional team of experts to boost the performance of all aspects of the lending business across Underwriting, Retention, Acquisitions and Collections at mPokket.
In the past decade, the BSFI (Banking, Financial Services and Insurance) sector has been undergoing a steady digital transformation. In this transition, big data and predictive analytics is playing a key role.
Big data analytics is driving greater organisational success by transforming business operations via reduced risks, seamless services and better customer experiences that assure increasing returns. The advent of 4G and growing pan-India connectivity has made gathering customer intelligence simpler.
Although the banking industry has been at the forefront of adopting emerging technologies, they were earlier unable to harvest data successfully or decipher key insights from captive customer information. But as digital tools have been mainstreamed, predictive analytics has helped India’s BFSI players to leverage customer data with greater efficacy. Data analytics assists BFSI companies in providing enhanced user experiences, improving customer acquisition and retention. For insurance firms, data analytics aids in underwriting risks and uncovering fake claims.
Whereas digital-native players were always aware of the tremendous benefits of harvesting data from customer touchpoints, traditional banks and other financial institutions have now also understood the importance of predictive analytics. Therefore, legacy entities are investing heavily in tech tools to catch up with digital natives. Through this, BFSI players can address three core challenges – improving performance, reducing risks and boosting profitability.
Predictive analytics leverages past and present data to predict unknowns and variables such as future customer activity and behaviour. For example, banks can use relevant insights from data to provide personalised credit cards to consumers by comprehending the likelihood of their inclination for this. Such data insights can ensure better outcomes in customer relationship management (CRM).
Here are six ways whereby predictive analytics can support BFSI firms in promoting healthy business outcomes:
1. Data Visualisation: Many companies hold huge amounts of data. Yet, they are unable to unlock its full business value. But business intelligence and data visualisation can help firms with actionable intelligence while discovering discernible patterns within large sets of data. Consequently, integrating advanced interactive visualisation capabilities within CRM and digital process automation solutions is now possible. This pinpoint hidden patterns, facilitating smarter business decisions and outcomes.
2. Fraud Detection and Prevention: Cybercrime remains a constant challenge in the BFSI sphere. While compromising customer data, it inflates the cost of combatting these frauds. Soaring digital transactions have led to an increasing number of financial cybercrimes. Backed by advanced algorithms and big data analytic solutions, banks and other financial firms can pinpoint unusual patterns and take action to minimise frauds and prevent potential crimes. Biometrics can also be supported via big data analytics for creating unique user IDs that can pre-empt financial frauds.
3. Customer Acquisition, Engagement and Retention: BFSI companies can find untapped consumer cohorts using data analytics. By fostering new loyalty methods, companies could enhance customer engagement and retention strategies. While customer loyalty has been a perennial challenge, these insights help in retaining customers by identifying those ready or willing to move to a competitor and the reasons behind this.
Analytics also helps by offering reports on consumer churn trends that help firms identify gaps in their products or services. By anticipating expectations, BFSI companies can consistently improvise in meeting them and attracting new customers. Behaviour patterns and spending habits can also be predicted. Such insights can then help in upselling and cross-selling products, generating greater profitability and enhancing customer relationships and retention.
4. Better Customer Targeting: Once the buying behaviour of customers is clear, BFSI players know the best products to pitch with insights provided by analytical dashboards. Banks can formulate targeted communication campaigns by analysing customer profiles and associated data. As a result, banks and insurers can tweak their messaging and provide customers with appropriate plans, programmes and offers. Targeted products and services then result in higher conversion rates. Anticipating and meeting expectations is one of the best ways to retain customers over the long term.
5. Efficient Collections Management: For BFSI companies, collections comprise an integral element to promote profitability. Keeping records of customers with outstanding payments vis-à-vis those paying on time can be a cumbersome exercise. By determining high portfolio risks in advance, data analytics simplifies and improves collections. Thereby, lenders remain better prepared to boost the productivity of collections by acting in time and as necessary.
6. Timely Feedback Analysis: Proper management of feedback augments analytical processes. In the digital era, customers expect bankers and lenders to comprehend and care for their needs. Despite the data deluge, predictive analytics assists in collecting consumer feedback and decoding the same. Through this, BFSI players can ascertain offerings are aligned with customer expectations. By adopting a proactive approach, companies can grow their business steadily every year.
By deploying predictive analytics, BFSI firms can garner all of the above benefits while also predicting potential customer trends and scenarios. Greater insights on changing customer behaviour and upcoming events can help banks and other financial firms be proactive, providing more streamlined services to consumers. Ultimately, this helps in propelling robust business outcomes.