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Top Artificial Intelligence Applications in Banking: Examples & Case Studies 

29 Sep 2020 | Nikhil Nair

What is AI in Banking?

Banks play a very important role in the development of the financial life of modern society. It is very important that each and every financial transaction done through the banks must be properly documented. AI in the banking industry can interact with humans by making decisions, get insight into customers’ preferences, to ensure that the customers are happy with the services provided by the banks, and help the customers understand their expectations from the banks.

Top Applications of Artificial Intelligence in Banking

• Anomaly detection can be used to increase the accuracy of credit card fraud detection and anti-money laundering
• Credit underwriting evaluate the risk of lending loans to customers
• Image/face recognition using real-time camera images and advanced
• AI techniques such as deep learning can be used at ATMs to detect and prevent frauds/crimes

Benefits to Banking

• Easy cash transactions (debit, credit )in less time
• Banks provide financial assistance or loans to people in need
• Online banking is an easy and effective way to manage money

Artificial Intelligence Trends in Banking

AI is strengthening the competitiveness of banks by the following:

• Enhanced customer experience
• Prediction of future outcome and trends
• Cognitive process automation
• Effective decision making

Customer segmentation

Segment customers based on types of products, number of investments to identify the most valuable customers to give more discounts or better offers.

Identify valuable customers

Identify key areas that need improvements and can help achieve a productive long-term relationship by analyzing customer feedbacks and reviews using advanced text analytics.

Customer Support

Prescribe products (RD, FD, MF, etc) to a set of customers by generating Smart Recommendations based on historical patterns, behaviors, and preferences.

Recommendation Engine

360-degree view across the past and present activities of customers by utilizing diverse banking products and services, their volume and profitability, and other geographical, demographic, and market data.

Life Time Value Prediction

Identify low, moderate, and high-value segments of customers who are likely to convert from low to a high value on the basis of life insurance coverage and premium paid.

Insurance Customer Targeting

CRM NEXT is a 360 view customer-centric application for banks that helps manage the entire customer life cycle. RMs, BM’s, Personal bank Authorizer’s maintain the generated leads and service interactions for the customers

Problem Statement

Excess of Information to be referred from CRM to know the customer & decide next cross-sell pitch.

Objective

1– 2 pager AI-based customer insights empowering RMs with brief insight across customer sentiments

Data

Service interaction captures first-line customer interaction needs and provides a fully integrated single screen solution. RMs enter comments based on the mode of interaction with customers. Branch manager review the comments entered by RM and give feedback to the customer by recommending products

HNW is a process derived from service interaction in which bank engage with the customers to understand their needs on products and addresses any kind of queries or services that is required by the customers

Methodology

Sentiment Analysis is an NLP technique that identifies and categorizes opinions expressed in a piece of text. It then determines whether the attitude towards a particular topic, product, etc. is positive, negative, or neutral. Natural Language Processing data standardization has the following steps :

* Remove Tagging: Perform the part of speech tagging wherein the grammatical words like noun, adjective, adverbs are tagged out to know the actual meaning of text or sentence
* Remove noise from the new dictionary by removing stop words that don’t convey any meaning
* Replace by Dictionary: Update the dictionary with post removal of the noise or unwanted text

* Regular Expression: Perform a pattern matching mechanism
finding the relevant sentiments from the comments
* Stemming & Lemmatization: an abstract form, shared by word forms having the same stem, part of speech, word sense – stands for the class of words with the same stem

Solution

• *There are comments entered by RMs, Sales Team, Branch managers as part of the service interaction
• The machine will understand sentiments from the text or comments
  i.e. Positive Sentiments like already done, Good Fund, Well Being already covered, and Negative Sentiments like Not interested, Contact No. not reachable, Net Banking, etc. Using natural language techniques
• The machine will be fed with the raw data for a period of time-based on which it will learn and predict the results

Problem Statement

To determine how much can be spent to acquire or retain a customer and on which groups of customers you should concentrate the most to maximize returns

Objective

Customer Lifetime Value (CLV) measures all the future profits a given customer will generate in total for banks. Knowledge of this value is crucial in maximizing business efficiency.

Data

Using Customer Profiler information, the bank can segment their customer & predict a personalized targeted product/s offering by grouping similar customers based on their household income, demographics, and Age group.

Methodology

Customer segmentation is the process of dividing customers into groups based on common characteristics like annual income, savings, gender age, etc. so that companies can market to each group effectively and appropriately.

Methodology(Cont’d)
Customer segmentation is the process of dividing customers into groups based on common characteristics like annual income, savings, gender age, etc. so that companies can market to each group effectively and appropriately.

Solution

Product recommendation solutions work as information filters that help to present products that are likely of interest to the customer.
Such services assist banks to boost sales and expand the number of returning customers.
Bank can segment their customer & predict a personalized targeted product/s offering using the Recommendation model.

Benefits

Planning campaigns to address the most valuable customers
Achieve better customer alignment and increase the impact on their purchases
Take the right step to maximize revenue per customer

Conclusion

With customer centricity being the key, a banking CRM has become the modern banker’s need. It is nearly impossible for banks to process and understand this data manually without a digital tool helping them out. In the quest for a customer-focused business, banks have started adopting CRM’s data using Analytics, AI models that help them to identify selling points that would not be possible otherwise.