Generative AI has seen a widespread adoption across multiple industries. However, when it comes to financial institutions, the adoption is relatively slow due to regulatory challenges and concerns. “For financial services providers, the risks and biases associated with large language models (LLMs) have even greater significance.

“As a regulated entity, we operate under various restrictions, particularly in terms of privacy. We have to adhere to strict data and consent frameworks to ensure the security and accuracy of the information we share. We don’t want to give out wrong information. So how we benefit from the generative model within our framework is something we are actively exploring,” Deepak Sharma, president & chief digital officer at Kotak Mahindra Bank told AIM.

Kotak Mahindra Bank is exploring all the options, from the GPT models by OpenAI to all the open-source LLMs available. “The emergence of Azure and other cloud platforms has brought forth a new wave of players actively exploring ways to deploy generative AI models within contained environments, utilising proprietary training datasets and enhancing them with LLM capabilities. This aligns closely with our current objectives and we are keen on leveraging such advancements to achieve our goals effectively.”

Sharma believes generative AI holds immense potential in various domains such as contact centres, code generation, conversational models, predictive modelling, and graphic visual designing. “These are the specific areas where we are exploring the application of generative AI, rather than considering it solely as an extension of our existing AI capabilities,” he said.

Impact on jobs

The introduction of generative AI has also sparked discussions around potential job losses, as is often the case with the emergence of new technologies. Similar concerns were raised when ATMs were first introduced, with widespread discussions about the potential impact on bank teller jobs.

“I think we have always seen whenever technology has changed, some kind of jobs get eliminated and a new set of jobs get created.”

“In any industry undergoing a transformation, it is natural for certain jobs to be phased out. We can anticipate a similar scenario with the integration of generative AI. However, it is important to note that alongside job eliminations, there is often an emergence of new and valued skill sets.”

As the landscape evolves, there will be a need for workers to upskill and repurpose themselves. “In today’s knowledge economy, this evolution is happening across various sectors, driven by factors such as hyper automation. Even prior to the discussions surrounding generative AI, hyper automation has played a significant role in reshaping job profiles and creating new opportunities. 

“Hence, in my mind, generative AI will make humans far more efficient and productive. For example, I’m not saying that my conversational AI or generative AI will replace the contact centre agent, but it will make contact centre agents far more efficient in how he or she interacts with customer.”

Banking & AI

Today, your banks are longer banks but technology companies. Over the last few years, banks in India have been investing heavily on technologies such as AI/ML to improve different aspects of banking operations from customer service to underwriting, risk management, and fraud detection. “We were the first bank in India to build an AI-powered voice base interface at our contact centre in 2019,” Sharma said.

He believes AI is set to revolutionise every facet of a financial institution’s operations. It is expected to have a pervasive influence, impacting all technologies presently employed within banks. AI will permeate various areas and processes within the banking industry, ushering in transformative changes.

It was during 2016-17 when Kotak Mahindra Bank shifted its focus to AI. Sharma revealed that some of the earlier AI models deployed by the banks were for propensity-based targeting of customers, enabling the bank to provide personalised offers and determine the next best offer for each individual. “We have also integrated these models into our credit decisioning processes. Fortunately, banks possess abundant historical data that can be utilised to train these models, allowing us to build a robust real-time credit decisioning engine. Over the past six to seven years, as we have expanded our use cases, the application of AI and ML has become more diverse and sophisticated.”

Furthermore, Sharma highlights that the optimal utilisation of AI at Kotak Mahindra lies in building and reinforcing the credit and risk model. Kotak Mahindra has also implemented AI solutions to combat fraud, bolster risk management practices, and fortify security measures.