Financial Services
Analytics in BFSI: Seizing the opportunity
31 Aug 2023
Businesses today use data analytics to make crucial business decisions. It has helped them to augment revenues, enhance customer experience and optimize their processes. The identification of behaviours and patterns (descriptive analytics), accessing the reasons behind them (diagnostic analytics), forecasting future trends (predictive analytics), and utilizing these findings as the foundation for future decision-making (prescriptive analytics) are all made feasible by analyzing historical data. This change has led to improved customer experience with cost-effective and time-saving business transformations

The BFSI sector is no different and there is a concerted attempt across the industry to leverage analytics at every stage of the business. The BFSI sector accounted for over a third of the total spend of US$ 30.5B (in 2022 alone) on analytics by companies (barring the IT sector) in India. Spending on analytics has surged with the hope that financial institutions can gain valuable insights and take informed actions. However, the pertinent question remains: Are financial institutions truly harnessing the full potential of data analytics.

2. Analytics in BFSI – unrealized opportunity?

Over the years, financial institutions have evolved from scrutinizing documents manually to using complex technologies and tools for their operations. Analytics in BFSI holds immense promise as a transformative tool in India. However, the true potential of analytics in the BFSI sector remains largely untapped. While some progress has been made in adopting basic analytics techniques, such as descriptive and diagnostic analytics - use cases credit risk analysis, fraud detection, etc., there is a notable gap in the utilization of more advanced analytics methods like predictive and prescriptive analytics.

Financial institutions are constantly improving in their efforts to realize the full potential of analytics and have been successful to some extent. Banks, for instance, are using advanced analytics during their pre-sales and sales journeys for retail loans, but they are still searching for answers to problems such as predicting customers who are likely to default and forecasting recovery patterns for loans. Moreover, the management is also looking for solutions to mitigate the identified risks, which will eventually decrease the probability of losses.

Following are some of the benefits that the BFSI industry can realize by leveraging advanced analytics:
  • Predicting customer needs: Financial institutions will be able to predict client requirements through consumer data analysis, and proactively suggest products and services even before clients realize their needs.
  • Personalized customer experience: Financial institutions shall be able to analyze and interpret customer data to gain insights and deliver personalized consumer experiences. For instance, by analysing a customer's historical transaction data, investment patterns, and credit behaviour, a bank can identify clients who can regularly maintain significant deposit amounts. The bank can then tailor their offers to provide richly customized wealth management services and offer suitable banking solutions to these high-value customers.
  • Efficient resource allocation: Financial institutions can recognize patterns and market movements with the aid of predictive analytics. Proactive allocation of resources such as staff, technology, and infrastructure according to the forecasted demand enables institutions to meet anticipated business needs. For instance, by predicting the demand for account openings in corporate offices, banks can strategically allocate more relationship managers (RMs) to cater to the specific needs of that location.
  • Customer lifetime value prediction: Estimating the potential value that a customer can bring to the institution over their lifetime allows financial institutions to prioritize and focus on high-value customers.
  • Customer churn prediction: Advanced analytics can empower financial institutions to identify customers at risk of churning by analyzing customer behaviour and engagement patterns. This enables institutions to proactively address customer concerns and enhance retention strategies. For example, if an RM notices a decrease in transaction frequency and a rise in customer complaints, he/she can proactively reach out to the customer to address their concerns.
  • Optimal Investment allocation: By considering various risk factors and market trends, prescriptive analytics can help financial institutions make informed and data-driven decisions to maximize returns and manage risks effectively.
The use cases of advanced analytics for an SME lender across a typical lending journey have been depicted below:
3. Leveraging the power of analytics – Key challenges
As financial institutions strive to leverage advanced analytics to their full potential, they encounter some challenges that demand careful consideration and planning:
  • Lack of clear objectives: Data analytics, by its nature, is an exploratory field, which oftentimes creates apprehensions about the return on investment. When management has this ambiguity, it inevitably leads to inefficient resource allocation, ineffective data collection techniques, and improper metric setting.
  • Misaligned objectives: There could be a gap between the priorities of the business and the analytics teams. The business team is primarily focused on achieving specific business outcomes, such as increasing customer acquisition, lowering churn rate, and increasing product adoption, whereas the analytics team, on the other hand, is largely focused on scanning through large datasets and generating insights, which might or might not answer the business' questions. Most business decisions do not involve rocket science, and there is a risk of overengineering the analysis.
  • Data inconsistency: One of the key challenges is poor data quality, which might be due to inadequate data collection, incomprehensible/unstructured data, or data being scattered around multiple data management solutions throughout the journey. This leaves the data team handicapped to generate meaningful insights. For example, in one of our recent projects, we were working on initiatives to reduce the query resolution TAT but ended up spending significant time and bandwidth in just setting the baseline of the number of queries received from their legacy systems.
  • Cultural resistance: When it comes to integrating analytics into current processes, there could be some obstacles. Existing stakeholders may resist changes due to their preference for established decision-making methods and legacy systems. Furthermore, subjectivity and expertise might outweigh data-driven outcomes in some institutions, which could create challenges for businesses attempting to implement analytics at scale.
4. Overcoming challenges
Though most financial institutions understand the benefits they can derive from analytics, they must also understand the need to undertake a few corrective actions to overcome challenges and meet their business objectives in the long run
  • Effective communication:  Fostering effective communication between the business and analytics teams and setting clear goals should be the key priorities for the management. This ensures that both teams are on track to achieve the same larger objective.
  • Focus on data quality and governance: Setting up stringent data collection and validation practices, as well as integrating data sources, is paramount. This ensures structured and consistent data throughout the data journey, enabling the generation of meaningful insights. Developing efficient data governance and monitoring mechanisms is key to organizational success.
  • Exploring partnerships: BFSI players can also explore possibilities of entering into partnerships with industry-accepted third-party data providers/aggregators, wherever required, to benefit from their rich and large set of data and ensure expedited implementation.
  • Data security and privacy: Storing sensitive personal data and information is regulated under various laws in India, primarily the Information Technology Act 2000. BFSI companies should ensure that their own data collection practices, as well as those of their external partners, adhere to the regulatory guidelines.
The BFSI sector has undoubtedly recognized the advantages of analytics, however, its true potential remains largely untapped. By taking proactive measures to embrace advanced analytics, financial institutions can position themselves ahead of the competition. Leveraging predictive and prescriptive analytics can enable them to make data-driven decisions, identify opportunities, and effectively address challenges. Financial institutions seizing the full potential of data analytics will undoubtedly lead the way in shaping the future of the industry.

Author: Shishir Mankad, Managing Partner & Head – Financial Services, Praxis Global Alliance
Co-Author: Sandeep Ghosh, Senior Manager - Financial Services, Praxis Global Alliance

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