Today, banks have more data to capture and analyze – one of the critical types being transaction data. And raw data on its own doesn’t offer the sort of actionable insights that banks would want to acquire. Then there is the treasure chest of textual data waiting to be tapped for intelligence. That puts Natural Language Processing (NLP) in perspective for financial institutions to reap insights from text as part of customer transactions.

With customer transactional data coming under the scanner, banks want to know more about customers by leveraging customer data – embracing the approach of integrating technology innovations into the banking system to understand behavior patterns and cozy up to the customer with new bills or loans.

Categorizing customer transactions, we can understand the key players in a domain. For instance, let’s consider the ‘insurance’ category. With the NLP-powered transaction categorization engine, banks can unearth the key insurance player and can work on building robust contracts to benefit from the partnership.

Putting the customer-first face, banks wants to create a winning blend out of technology and transaction to enable enriching customer experience – making it easy for using banking services. Whether it is about knowing more about the customer or enabling customer experience, NLP-powered transaction categorization engine empowers banks to maximize value from customer transactions.

The ‘How’ of Reaping value from Customer transactions

There are customer transactions and the text that can offer valuable insights. Initially, Natural Language processing helps the bank strike it rich with the text and gain value from customer transactions. Furthering the NLP initiative, Machine Learning or Deep Learning can be leveraged for model building to achieve classification of customer transactions into individual buckets.

Firstly, the historical data is pumped into pipelining of Text pre-Processing, followed by data cleansing, transformation and model building. Models with most effective classification capabilities can be built and deployed and can be integrated with various platforms including mobile apps or web services, or even integrated as an augmented feature into the existing platform

Twin Benefits of NLP-powered Transaction Categorization

The following demonstrates how the banks can use the text data and reap twin benefits out of it. By classifying banking transactions and bucketing them into individual buckets, banks get closer to customers and earn their loyalty.

For the customer

At a customer level, it becomes a tedious task to keep track of the amount spent on different items involving different scenarios. Empowering customers in this scenario means embracing a better approach to create a system that automatically categorizes customer transactions and facilitates independent categories. The system not only reduces the customer burden of keeping track of the amount spent but also provides an analytical report that helps customers keep tab on their spending – analytical report with all the dashboard visualizations of customer’ spending on demand is an enticing feature that benefits customers.

For the bank

At the Bank level, cognitive behavioral patterns work to the advantage of banks as a result of transaction categorization. Now, there’s more potential to understand customer behavior where patterns that are read can be aligned with timelines, which enables banks to predict customer spending. And with that a marketer more about the customer can come up with alluring offers encompassing seasonal offers, credits, debits and lot more. Through transaction categorization, banks also gather insights revolving around crucial players in various domains covering Retail, Insurance and others, which in turn can win loyal customers.

Transaction categorization engine and number of categories

Though fewer categories mean less errors, greater level of insights can be acquired only by creating more number of categories. For instance, having just a single category for apparel will not yield desired results, wherein sub-categories like shirts, trousers, T-shirts, skirts, blazers and jeans among others need to be considered.

For an organization, a global expert in the areas of categorization and data capture, we took the shortcomings of the existing categorization engine into account – limited number of categories seem to work against the results that were delivered – and created more than 140 categories that proved pivotal in producing accurate output.

NLP-powered transaction categorization augmenting other KPIs

Natural language processing also provides thrust to the existing ML/AI approaches for KPIs that matter most to banks. With NLP made to work on top of ML/AI approach that banks adopt to strengthen KPIs, it can result in the augmentation of KPIs, better accuracy and results achieved out of the KPIs including

  • Churn management
  • Credit decisions
  • Risk assessment
  • Fraud prevention
  • Personalized approach
  • Customer Segmentation

For all the KPIs mentioned, there is one thing common and that is the ‘Customer’. It is a requisite to know more about the customers and their behavioral patterns from the data available. And with transaction categorization enabled through NLP, there is more customer intelligence flowing covering dynamic events of customers thus empowering banks to reap rich rewards leveraging all  of the above and more KPI’s.

And the shining example is the ‘Customer segmentation’, where historical data covering loans, FD, default and non-default among other features become prime feed. For a ‘dynamic’ version of customer segmentation, what becomes the key is to read the dynamic behaviors of customers. With NLP powering transaction categorization, what becomes the core is the dynamic nature where daily transaction and the types are captured to facilitate accurate customer predictions.