By Dennis Gada
Financial sector spending on Artificial Intelligence technology is growing rapidly, with estimates suggesting that investments, that are currently at about $45 billion, will cross $126 billion in 2028.
Operational efficiency is, most often, the low hanging fruit. Financial service organisations are automating routine operations – data entry, transaction processing, customer service, reconciliation, reporting etc. – to save millions, dramatically improve turnaround time, and slash error rates. Back-office activities, such as document checks and customer identity verification, are being performed using AI, ensuring faster, cleaner processing at lower cost. At the front-end too, AI tools are automating a variety of time-consuming manual tasks.
Beyond improving efficiencies, AI is transforming financial services by providing rich data insights in real-time, enabling organisations to hyper-personalise offerings, provide frictionless experiences and take faster, better-informed decisions. Further, since AI systems learn continuously from data to improve the reliability, confidence-level and granularity of predictions, banks can manage their short-term liquidity requirements better and reduce idle cash. Also, Predictive AI and Machine Learning models can assess the impact of internal and external parameters on cash inflows and outflows, to support stress testing, scenario planning and other use-cases.
AI is also helping banks better manage different risks, from credit risk to operational risk to currency and market risks. For example, AI algorithms study data, such as utility bill payment patterns, rental history and social media activity, to assess the creditworthiness of customers without a credit score, enabling banks to lend to more people.
While AI can be exploited to perpetrate sophisticated crimes, it also provides the most effective defence against fraud. Machine Learning-powered tools can spot anomalous patterns – transactions originating in an unusual location or repeated failed logins – in real-time to detect breaches and prevent fraud, thereby minimising financial losses. Constantly learning from new data, the solutions improve in accuracy over time, flagging suspicious activity while minimising false positives.
Struggling with the effort and cost of compliance, banks are finding a welcome solution in AI; AI-powered tools can automate compliance monitoring, regulatory reporting, and other processes to ensure financial institutions adhere to current mandates and are also better prepared to meet future requirements. AI-automated data collection, analysis and reporting streamline compliance activities, while audit trails bring transparency to compliance operations. At the same time, AI is building financial sector resilience by providing visibility across enterprise operations, conducting probable cause analysis, predicting risks, and accelerating remediation of issues. Here again, AI tools analyse vast quantities of data to anticipate resilience risks, enabling financial institutions to proactively address them.
But although every bank is using some AI, few have managed to scale it across the enterprise. Most organisations have acquired solutions ad-hoc, without taking a holistic, integrated approach to adoption. To unlock the real value of AI, financial institutions need to move beyond isolated implementations to embed the technology throughout their core operations. They should also provide the right environment and resources for successful AI – modular, scalable infrastructure, strong data foundation, and a culture that values human-AI collaboration.
The writer is EVP and global head of banking & financial services, Infosys
