Introducing Analytics in banking has changed the entire perception of Banks. They are no longer just about opening a savings account or taking a loan; it has become more of a personalized experience for customers. Compared to industries like healthcare, CPG, telecom, etc., analytics is extensively used in the banking sector. Previously, only large sectors had the provision to leverage analytics and MSMEs limited themselves because of several restrictions. However, after COVID-19 endemic, even small and mid-sized enterprises are considering analytics.

What drives analytics in BFSI?

The major triggering aspects for enterprises to consider analytics are:

  • Need for customer-centric offerings and solutions because of increased customer expectations.
  • Need for better fraud management and adaptability to the volatile business environment.
  • Need for an agile process to leverage the massive amount of structured and unstructured customer data.
  • Need for insights-based actions to cope up with the increasing competition.

Types of Analytics for Banking

We can divide analytics based on the following categories (Source: Everest):

  • Operations
  • Risk
  • Marketing
  • Performance Management/Planning
  •  Customer Relationship Management

The 4 main traditional types of data analytics in banking are:

  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics
  • Reporting Analytics

In order to gain maximum understanding of the above four analytics, we can elaborate them into:

Customer Analytics

In order to come up with customer-centric solutions, banks have to first get a holistic view of customer needs, understand their pain points, and provide solutions that can directly cater to customer requirements. Customer analytics leverage past and present, structured and unstructured customer data, classify customers based on this data, and provide detailed customer insights.

Banks can use these insights to provide better services like faster customer onboarding, seamless loan processing services, etc. Banks can also go a step further by providing spend analysis using PFM, provide insights on loans and other aspects based on customer’s spending and income ratio, etc., and overall provide efficient customer support.

*Using Customer Analytics results in:

  • 40-45% of productivity saving in over 3 years
  • Approximately 15% improvement of first call resolution
  • 15% reduction in data management cost
  • On-time delivery achieved over 97% against the benchmark of 90%
  • 10% estimated drop in loan processing time

Fraud Analytics

Fraud management has always been a daunting task for bankers. With increasing digitisation, bankers need to find new and innovative ways to reduce frauds and other cybercrimes. One of the effective ways to tackle such scams is with fraud analytics.

Fraud analytics usually works by analysing the patterns of fraudsters and try to decode insights to find preventive measures accordingly. Using fraud analytics, bankers analyse the patterns using past data, and with other insights using deeper analysis, banks can take actions. Common measures are having restrictions on the spending limit, two-factor authentication, etc.

*Using Fraud Analytics results in:

  • 13% reduction in fraud losses
  • 17% increase in recovery rate
  • US$6 million realized in annual savings and US$9 million loss avoidance
  • US$100 million reduction in fraud-related costs

Risk Analytics

Credit risks are the major setbacks for most banks to invest in the lending business. Previously, bankers analysed credit risks based on past experiences, which was a manual job. However, with the rise of different loan categories and number of applicants, banks need a smart tool to analyse and provide accurate credit scores.

Risk Analytics calculate the risk factors based on past data and come up with factual insights. Based on this, bankers can invest in lending and scale their products accordingly.

*Using Risk Analytics results in:

  • Approximately US$20 million reduction of risk in the lending portfolio
  • 20% reduction in false positives
  • US$50 million of Net Credit Loss (NCL) saves

Marketing Analytics

The best of best banks can lag with respect to customer acquisition in the absence of effective marketing. Right marketing helps banks to approach the right customers. However, in order to achieve targeted marketing, banks need to have insights regarding customer requirements and work on their offerings to suit customers.

Hence, sales and marketing analytics is used in banks to improve sales and target the right products for the right customers. The insights gained from marketing analytics concentrate on the real-time requirement of customers, approachable timings, customer lifetime value, etc. This will help banks to customize their offerings, reach out to the customers via different campaigns and increase cross-selling and up-selling.

*Using Marketing Analytics results in:

  • 50% improvement in conversion rate
  • US$24 million worth sales leads identified in the first 6 months
  • US$1 million productivity gain
  • 3.5X increase in campaign ROI

Business Analytics

While the above analytics help to improve banks on the customer forefront, business analytics provides insights on business efficiency. Business analytics provides different strategies to help optimize both business and customer facets.

Business Analytics provides insights on missing aspects of products and offerings, enhancements to improve ROI, improvements through customer feedback, etc.

*Using Business Analytics results in:

  • 6% increase in total revenue
  • 50% reduction in spend per operator/month
  • 2.97% reduction in the cost of collections

Future of Analytics

Analytics in banking has proven to be an advance tool to excel in the field. With the combination of proficient technologies like cloud management, AI/ML, etc., the applications of analytics in banking can shape a new world for banks.

Cloud technology helps to organise and store the data helping analytics to become more accurate and provide expert insights to aid banks. AI/ML helps in rapid processing in each stage of banking, which helps analytics to decode in real-time and provide both business and customer insights to bankers.

A Takeaway

Analytics has become bankers’ safe-hub and most banks are betting on it and moving towards innovation. Hence, it is important to have the right expert who can help you with the right analytics solution. Meet ‘Analytics for ME’- Aspire’s analytics expert who can help you provide a 360-degree view of customers and their requirements. Our data-driven solution rightly points out all the pain points of customers and business, providing accurate insights on enhancements.

*Source: Everest Group