Analytics in BFSI: Seizing the opportunity
31 Aug 2023
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
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.
in BFSI – unrealized opportunity?
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.
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
are some of the benefits that the BFSI industry can realize by leveraging
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.
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.
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.
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.
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.
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.
use cases of advanced analytics for an SME lender across a typical lending
journey have been depicted below:
the power of analytics – Key challenges
financial institutions strive to leverage advanced analytics to their full
potential, they encounter some challenges that demand careful consideration and
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.
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
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.
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.
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
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.
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.
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
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.
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