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The Game-changing Role Of AI In Banking

rtificial Intelligence (AI) is emerging as an essential tool for bankers as they strive to deliver personalised and seamless experiences to their clients while improving their operational efficiency

Photo Credit : shutterstock


The failure of large US Banks this month highlighted the criticality of risk analysis and its proactive management. However, in a VUCA world, the business of managing risk is easier said than done. Rapid technological advancements, changing customer preferences, geopolitical instability, fierce global competition and regulatory changes are among multiple factors that have created a highly dynamic and unpredictable environment for banks. 

With digital transformation, banks are innovating with emerging technologies to remain competitive and meet the evolving needs of their customers. Artificial Intelligence (AI) is emerging as an essential tool for bankers as they strive to deliver personalised and seamless experiences to their clients while improving their operational efficiency. AI algorithms can analyse large volumes of data in real-time, identify patterns and find outliers - enabling banks to make better informed decisions and predictions based on customer behavior, market trends and risk management practices. 

A Bank for International Settlements working paper provides evidence from a Chinese fintech firm that ML-based consumer credit scoring outperforms traditional linear models by reduction of total losses in the range of 6-25 per cent. A case study published by imaginovation, estimates that JP Morgan made a significant cut in 3,60,000 hours of lawyers’ and loan officers’ fees each year by automating the mundane task of interpreting commercial-loan agreements. National Australia Bank (NAB’s) saved USD 1.2 million in costs within seven months of deployment of an employee chatbot that supports them in answering customer queries.

As per a 2021 OECD report, the deployment of AI shall drive competitive advantages through two main avenues: one, by improving the firms’ efficiency through cost reduction and productivity enhancement, therefore driving higher profitability, and two, by enhancing the quality of financial services and products offered to consumers.

Several other use-cases of AI have surfaced and are proving to be beneficial. One area where it has seen widespread adoption is service automation. Powered by underlying AI, chatbots and virtual digital assistants (VDAs) on web and mobile apps are changing the way banks deliver customer services. It is not just an automation of banking and account related queries and services, but also beneficial in marketing add-on services or automating paper-based process like loan applications etc. 

When integrated with data analytics and decision algorithms, service automation provides an opportunity to complete the entire process from application, credit checks to approval without any human intervention. Automation provides faster and efficient 24x7 service, resulting in improved customer satisfaction and loyalty as well as enhanced business performance.

Wealth Management is another area where AI has made a significant impact. Leveraging sophisticated algorithms and machine learning models, AI-driven wealth management platforms recognize profitable investment opportunities and provide tailored investment advice and portfolio management services that align with individual customers' unique risk appetite, life goals and investment preferences. Relationship managers are empowered to provide a more comprehensive and informed investment strategy and ensuring that investment portfolios are optimised for individual needs. 

AI also works in conjunction for marketing of banking products and services, as algorithms recognize and gain a deeper understanding of their customers' needs and preferences enabling banks to create precise customer segments and develop personalized offers and services that cater to individual wishes. Marketing can run targeted campaigns, delivering relevant messages to specific customer segments based on their interests and needs like customized investment recommendations and financial planning services.

Many applications of AI driven predictive analytics are now being implemented in Banking. In sales and marketing, it is helping to gain insights into customer behavior, identify market trends and implement personalised marketing. Beyond boosting customer loyalty and experience, predictive analytics is used in the corporate back office for risk management, fraud detection, credit risk assessment and to predict future outcomes. Banks invest considerable time and resources to manually examine large data volumes to identify potential risk factors and alleviate emerging ones. Instead, AI-powered risk management systems use machine learning (ML) algorithms to analyse historical data plus monitor transactions in real-time, flagging any suspicious activity and preventing fraudulent transactions.

Cybersecurity is another top priority for banks as they hold sensitive customer information and are prime targets for cyber-attacks. AI is a formidable ally for them in their cybersecurity defense. Intelligent algorithms continuously monitor and analyze data, identifying potential threats or anomalies and quickly respond to security breaches. This real-time threat detection and mitigation capability is critical for banks, as it allows them to quickly identify and neutralise cyber-attacks before they can cause significant harm. Moreover, AI can help banks strengthen their authentication processes through biometric identification and advanced analytics that prevent identity fraud. 

Another innovative use of AI is in keeping track of financial transactions to detect possible money laundering, terrorist financing, and other financial crimes. Timely alerts raised by AI algorithms that monitor transactions help in maintaining compliance with regulatory requirements and preventing violations minimizing the risk of non-compliance. 

Managing banking business amidst this multi-faceted market forces can be a daunting task. Banks must embrace agility and adaptability, continually monitor and analyse data to identify potential threats as well as opportunities and quickly adjust their strategies to remain competitive. From front office to back, from customer experience to cybersecurity, fraud prevention to risk management – AI is enhancing the speed, precision and effectiveness of banks, making them more responsive, resilient and profitable.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

Tags assigned to this article:
banking sector Artificial Intelligence (AI)

Anurag Sanghai

The author is Principal Solution Architect, Intellicus Technologies

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