Advanced Analytics In Banking Industry

5 Definitive Use Cases For Advanced Analytics In The Banking Industry

The banking industry has made significant progress over the years by leveraging data. We have observed that advanced analytics has emerged as one of the key disruption in the financial services industry. According to a McKinsey Global Institute study, AI and Machine Learning have the potential to create between $167.2 billion and $344.9 billion in value annually across a range of business functions in the banking industry. The potential impact as a percentage of sales is 2.5% to 5.2%, making it one of the top industries that ranks highest on the analytics maturity index.

Placing a greater emphasis on analytics has also assisted banks in efficiently managing cyber security, enhancing customer experience, driving sales management strategies, fraud detection and financial risk.

Advanced analytics is enabling banks to attain superior performance across all business functions and drive measurable growth. Here are 5 interesting advanced analytics use cases for banking that illustrate how this data science technology is transforming the industry:

1. Customer Acquisition

Banks can develop real-time insights into individual prospects and personalize engagement by investing in advanced analytics tools. The actionable and accurate insights gained from these tools will help to generate interest in your products and services on a customer-by-customer basis and improve customer segmentation.

For example, Citibank is a strong advocate of data-led, analytical approach and often experiments with innovative use cases of analytics by deconstructing data. One primary use case is customer acquisition and retention. The bank has analyzed its customer data with machine learning algorithms and used this analysis to target promotional spending.

With analytics, you can explore historical performance of new customers, measure campaign effectiveness, estimate the potential value of each customer, estimate price sensitivity and personalize the acquisition offer.

2. Customer Retention

The cost of customer retention is much higher than the cost of acquisition. Therefore, it is important to solidify the relationship by meeting and exceeding expectations. The lifetime value of a customer can be maximized by developing an effective customer retention strategy. This would turn them into profitable and loyal banking customers.

In order to achieve this objective, banks need to assess the customer’s economic value, behavior patterns, preferred service channel, customer attrition level and feedback. By running predictive analytics on these elements, banks can identify silent attrition, and dissatisfied customers and create strategies to tackle this situation. With analytics insights, you can roll out preventive measures for customer retention and reduce attrition and dissatisfaction.

American Express serves as a great example of how to forecast potential churn and localize strategies to retain customers. The global financial services company relies on big data tools and techniques to empower business decision-makers to act locally. American Express also analyzes cardholders’ spending patterns to provide customized offers and retain customers. Analytics-driven targeted marketing allows the company to match the right customers with the right merchants, resulting in loyal and profitable customer base. And the company is also able to predict a possible churn and design marketing strategies to convert them into life-long customers.

3. Fraud Analysis

According to KPMG’s Global Banking Fraud Survey 2019, external fraud has increased, both in value and volume. As a result, fraud detection and prevention is the top priority of banks. Advanced Analytics has replaced the manual process with automated data-driven technology to monitor transactions and activities and detect fraud.

By identifying transaction irregularities and dubious activities using customer data, analytics tools can flag and investigate financial crimes like fraud, money laundering and criminal financing activities. Predictive analytics also help banks to study customer behavior and identify suspicious activities and protect accounts against repeated cyber-attacks.

For example, if a customer has informed his/her bank that they will be travelling out of the country, any financial activity outside the customer’s registered location and current location can be noticed immediately. This will allow banks to send alerts in real-time and take quick precautionary actions such as alerting the customer and freezing the account.

4. Credit Risk Analysis

Internal and external fraud combined with market volatility and customer default make banks and financial services vulnerable to a number of risks. Therefore, risk management plays a crucial role in minimizing risk exposure and protecting the value of its assets.

According to Deloitte research, regulatory reform is one of the three main drivers behind the wide adoption of advanced analytics in banking, when it comes to risk analysis and management.

Besides managing operational risk, market risk and liquidity risk, banks use big data and analytical techniques to strengthen their credit risk management process. While we have listed the use cases for external fraud detection, financial organizations can eliminate risk at the customer acquisition and retention stage by evaluating new customer accounts. Factors that advanced data analytics can help to determine are:

  • If a new account was created by furnishing false information
  • The likelihood of a customer defaulting on a loan product
  • The most effective default management tactics based on each customer profile
  • The expected value at risk from potential customer loss. 

5. Cross-Selling

Banks are able to boost cross-selling and up-selling opportunities of their products and services with predictive analytics. With access to information about customer behavior at a granular level, banks can prepare hyper-personalized strategies, which would have been a strenuous task in the absence of advanced analytics. For example, banks can not only measure the effectiveness of cross-sell campaigns by conducting a win/loss data analysis, but also anticipate acceptance rates for future cross-selling initiatives.

Yes Bank, whose big data analytics use cases received global recognition at Gartner Excellence Awards, uses advanced analytics and data science techniques to map the customer life-cycle and obtain insights into customer behavior. They used these insights for their marketing and cross-selling campaigns and portfolio management tactics.

A Large Financial Services Company Leverages Predictive Analytics To Increase Revenue Through Cross Selling

One of our clients, a large financial services company based in India wanted to increase sales through cross selling of their insurance products. They wanted to analyze their CRM data, identify customer trends and predict which customers are most likely to purchase based on their previous buying patterns of other products, age, profile etc.

Acuvate helped the client set up a  BI & analytics help desk  for  creating  Excel-based predictive analytics reports. The reports showed which customers are more likely to buy  the insurance product based on their profile and buying patterns.

The initiative significantly increased the client’s revenue, conversion rate and reduced the spend on costly statistical tools.

Learn More About This Case Study

Analytics and The Changing Banking Landscape

We have witnessed that customer expectations are rising in line with evolving technology. The use cases discussed above demonstrate how advanced analytics technology is a huge differentiate in today’s rapidly changing banking industry. If you’d like to learn more about use cases of advanced analytics in the banking industry, please feel free to get in touch with one of our Data and banking experts for a personalized consultation. You might also be interested in exploring our business intelligence and analytics solutions and services

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