Page 34 - Banking Finance November 2021

ARTICLE

Operations and Performance Manag-

ement:
Operations management is one such driver which involves
a series of analytics that can be considered such as supply
chain analytics, claims analytics, call center analytics, work
force analytics, IT operations, spend and usage behavior
analytics. All these focus on product and portfolio
optimization that determines prepayments, misbehaviors
defaults and cash flows to the banks. These analytics shows
better impact on profitability of the banks thereby helps in
smooth flow of operations.

Customer Management:

Under customer management of banks we come across
current position. Complex and time series data is considered market sizing, segmentation and targeting, customer
acquisition strategy, cross sell and upsell opportunities,
for applying basic set of statistical and mathematical tools
marketing mix and optimization leading to channel
to study the data behavior and draw minor conclusions. For
example, customer segmentation and profitability, campaign performance, campaign and sales effectiveness,
analytics, value at risk calculations etc. customer satisfaction from customer lifetime value (CLV)
estimation, digital experience of customers product

Predictive Analytics: These analytics predicts the likely comparison and attributed sentiment and tracking
future outcomes of the events. Here the big data is sentiments in future, brand equity and trends information
considered being real time and from various sources known from social media and digital media and finally real time
and unknown. Accordingly, advanced and specialized tools offers and personalization.
are considered for predicting the future possibilities.
Risk Management:
Prescriptive Analytics: These analytics prescribes the action Risk management analytics modeling involves analysis of
on the predicted outcomes for a situation. Still more various portfolios to forecast likely losses and make provisions
advanced techniques are used for prescriptive actions on the for those adequately. It comprises of risk assessment,
predicted outcomes and it promotes self-learning. For scoring and rules, credit risk, AML, KYC, loss forecasting,
example, behavioral probability defaults, loss given defaults, default management, collections analytics, regulatory
exposure at default modeling, stress testing for mandated requirements in relation to Basel and CCAR, trade
and custom scenarios etc. cancels and settlements etc. Early warning signals of both
customers and banks are sent in case of any mis-happenings
Model Framework for Analytics in or finding such preventive actions for protecting from AML
incidents.
Banking
The key areas where analytics in Banking impacted a lot Regulatory Governance and Compliance:
are:
Y Consumer and Marketing Analytics Due to stringent regulatory environment there is rising cost
of compliance and also risk of non-compliance in some cases.
Y Risk, fraud and Anti-Money Laundering / Know Your
Under regulatory and governance compliance analytics
Customer Analytics proper regulations are followed by the banks and there is a
Y Product and Portfolio optimization modeling. check if any deviation is there in the operations or any issues
relating to the customer activities thereby protecting the
Accordingly, a frame work model can be designed with basic governance of the banks. This ensures trust on the banks
drivers / components of banking data analytics being - from the customers.

34 | 2021 | NOVEMBER | BANKING FINANCE