Data analytics has become the key determinant in predicting customer needs and behaviour

Jasjeet Singh, Partner, Financial Services Analytics Advisory at EY throws light on how data analytics is changing the landscape of the banking and financial services sector.

Excerpts:

Q. How is the BFSI sector betting big on data analytics?

A. The proliferation of big data, faster computers and innovation in analytics has opened new opportunities for banks to better understand the past, control the present and confidently embrace the future. Banks today use analytics for fraud detection, credit risk decisioning and marketing. However, a lot remains to be done to turn their data into actionable insights. As customer interactions become more electronic and distant, banks need new insights into customer behaviour. By deploying advanced analytics techniques banks can predict with reasonable accuracy customer needs for a banking product/service at any point in time.

While historically data was key to supporting customer transactions, in recent years, the power of analytics has transformed the way data is creating impact in the functioning of financial organisation. Predictive models, smart customer segmentations and AI/ML led solutions have not only been helping financial organisations to manage their risk better but also provide enhanced customer experience thereby increasing profitability. In a country like India, where a vast population is still underserved when it comes to financial needs, data analytics has started playing a key role in creating a more inclusive eco system. To cater to the diversity of our country, deep learning and Natural Language Processing-based bots are enabling richer interactions in vernacular languages. The complexity of Indian ecosystem along with continued disruption by fintechs, entry of new players like payment banks and customers demanding an experience like Uber and Amazon, the traditional financial way of working is fast disappearing. BFSI sector is ‘banking’ on digitalisation and data analytics to create an exponential impact.

• Effective usage of Big Data and analytics tools can enable banks to enhance their credit underwriting processes by tapping into newer data sources including e-commerce transaction data, travel aggregator data, social media data etc, to help underwrite risks for customers with low credit score or non-existent bureau scores. This will help banks cherry-pick credit worthy customers from a segment of customers that had historically never been given credit because of lack of sufficient information for banks to assess their risk.


• We also see an opportunity for expansion in breadth of analytics usage from traditional focus areas of risk and marketing into hitherto untapped avenues of – HR (to improve effectiveness of employee recruitment and retention), Compliance (moving to 100% transaction monitoring instead of traditional sample based testing approach to detect anomalies), Operations and Finance functions.


• We work with banks to identify opportunities that can deliver profitable growth while protecting and optimising their enterprise. Drawing on our deep banking experience and advanced analytics competency, we have put together the Banking Analytics Playbook as a compilation of Use Cases across Banking Value chain where analytics based interventions can help you drive your Bank’s strategic imperatives including-Improving customer-level profitability, Reducing NPAs, Increasing wealth management product penetration among HNI customers, Improving NIMs by focusing on low-cost deposit growth etc.

Q. To build a sophisticated analytics capability, what is the kind of investment BFSI and Fintech companies are looking for in order to scale up the technological infrastructure, strengthening the processes and optimise the business decision making?

A. There are 3 areas where financial services players are looking at investment for building sophisticated capability – Data, Infrastructure, and People.

• DATA: Data is at the centre of analytics-led innovation. The organisations are investing in not only bringing all the existing data sources together at one place but also trying to identify which new data source can be created and added. In the process, the boundaries of structured and unstructured data is blurring. There is strong focus on identifying new external data feeds, like social media and telecom which can enrich the insight generation process.


• INFRASTRUCTURE: To support the changing data composition, which includes the variety, veracity and volume aspects, organisations are looking at strengthening the infrastructure foundation. This also needs to be complimented with a layer for decision support system and advance analytics. From what used to be a taboo area due to security concerns, financial services have now become more open to open sources tools and cloud-based solutions.


• PEOPLE: People capability would be key to using the data and infrastructure to create competitive advantage. Organisations are investing in hiring right talent and training them. They are adopting both inhouse and external model to support the rapidly evolving need of skill sets.

Q. What are the typical challenges marketers face in realizing the value of analytics? What are the steps to address those challenges?

A. The marketers are now moving towards enabling up-sell / cross-sell to the customers driven by algorithms that will help automatically - (a) Identify and prioritise targeting acquisition efforts,
(b) Optimise lead allocation for fulfilment, (c) Optimise whom to call? When to call? What channel to use for the campaign? What time to call a customer etc? However, the key challenge for the marketers lies in:

• Integration of prescriptive analytics output with business processes to ensure value realisation


• Sourcing and storing data across entire business value chain; and innovating in additional areas for data capture.

Understanding customer behaviour at an individual level along with when to call and through which channel, and how to continue taking feedback and learn is a challenge that AI-based platforms can help solve. This requires leadership vision and collaborative working of various departments touching customer. Another challenge is real time interventions. Almost all the financial services organisations still depend on batch processing for customer interactions. Making it real time will not only enhance the efficiency of outcome but also enhance customer experience. Financial services also need to move away from product centred approach to customer led strategy and interventions.