By: Aditya Kumar, Founder & CEO Qbera.com
Banks and financial institutions invariably have to deal with large volumes of data in the form of customer information, transaction histories, monitoring, and reporting. Coming to think of it, it is no surprise that conventional data-processing applications are literally no good in managing such large volumes of data. This is precisely why Big-Data analytics, along with Artificial Intelligence technologies, is a necessity to allow financial institutions to function seamlessly.
The banking system has evolved significantly over the last many years, and the adoption of Big-Data Analytics, Blockchain, and Artificial Intelligence is part of the most recent evolutionary trend. In this article, we look at how effective Big-Data processing and incorporation of Artificial Technology are in the banking sector, and how they’ve become a heightened necessity in the present day.
In recent times, banks have seen the requirement to switch from a product-centric approach to a customer-centric approach in order to keep sizeable market shares in a fast-evolving and highly competitive lending market. Big data has a massive role to play in this process, as managing massive volumes of customer data digitally is no small deal by any margin. Big Data analytics, in congruence with Artificial Intelligence, enables banks and lending institutions to manage complex and voluminous data, while also assisting in several other aspects including fraud detection, risk management and regulatory compliance, among others.
Big-Data Analytics and AI to understand credit behaviour
One of the foremost application of big data and AI is to evaluate customer behaviour and determine a customer’s overall credit health. Millions of customers will have millions of credit accounts in their names – so you can begin to imagine the enormity of the data in question. Big-data processing technologies make it possible to record every action of a consumer (in terms of repayments – late payments, timely payments, defaults, etc.), subsequently allowing banks and lending institutions to make a decision on approving/rejecting a credit proposal by understanding the customer’s credit behavioural patterns.
Note that Artificial Intelligence plays an equally important role in this regard. Algorithms designed to evaluate risk are a norm these days – these technologies use artificial intelligence to assess future credit behaviour from already available data. For instance, if a consumer applies for a personal loan, information on the customer’s credit profile is obtained instantly, and a decision on whether the customer is creditworthy or not is also made almost immediately, and automatically! This seamlessness in the application process has been made possible only through big-data analytics and artificial intelligence.
Detection of fraud
Banking systems are till date, even after evolving so much, vulnerable to fraudulent activities and transactions. Artificial Intelligence helps a great deal in this regard, primarily to help detect instances of fraudulent activity. Using big data and analytics, a customer’s general repayment and credit behavioural patterns are understood and recorded, and any deviation from the usual behaviour will trigger an alert, thereby helping to detect cases of fraud more effectively. Almost every financial organisation that is active in the lending space has integrated Big Data and Artificial Intelligence technologies to minimize instances of fraud and protect themselves against potential threats.
Risk management is a core area in several industries, with the banking industry attracting the largest necessity for potent risk management tactics. It goes without saying that Artificial Intelligence and predictive analytics of big data is immensely important to devise and incorporate powerful risk-management systems and deduce effective risk-management tactics.
Regulatory Compliance and Reporting Financial services are bound by extensive regulations that require constant monitoring of data and consequent reporting. Big data technology helps to manage, document and process large-scale customer-related information (and other forms of information as well), and undertake subsequent analysis and reporting in compliance with regulatory frameworks.
Tuning products to customer behaviour
Another impressive way that big-data has helped banks and lending institutions optimize their functioning is through understanding customer behaviour and knowing exactly what they want. Predictive analytics and artificial intelligence are crucial in this sphere, as understanding the behaviour of numerous consumers individually is a task that is near impossible if not for the emergence of these technologies.
Lenders also employ these technologies to categorize unstructured data and deduce behavioural patterns. For instance, unstructured data from social media profiles can be used to comprehend a consumer’s spending interests, products and services of interest, and other aspects which can potentially determine credit management patterns – AI technologies are used to achieve this.
As such, it is solely because of the integration of big data analytics and AI technology that the banking system has been able to evolve to a level that it is today! Further, with the constant evolution of independent analytics and AI, there is indubitably more in store for today’s consumer.