Humanising Financial Services with Artificial Intelligence

It takes a clear business strategy and a solid operational plan to use AI and ML to build experiences and produce revenue.

FREMONT, CA: A growing number of financial services executives are proposing AI  and ML for the advancement of customer experience and operations. Even though the entailing complexity of implementing these technologies makes it sceptical, these technologies, once implemented, play a crucial role in developing digital enterprises.

Though the pandemic propelled digital and the industry met clients' needs effectively and efficiently from a tactical and transactional standpoint, it lacked warmth and resonance in the digital channel. By introducing humanisation into digital interactions combined with in-person contact, AI assists in delivering more individualised, more pertinent services that are in line with customers' needs.

Using AI and ML, executives hope to provide clients with more individualised interactions and services regardless of the type of engagement they pick, whether it is sales engagement, servicing or retention engagement.

While AI holds the potential to expand the range of services that financial institutions may offer, there are obstacles to overcome, such as skewed expectations, skills issues, and implementation issues. Another central limiting element is the need for more talent, be it the leaders and resources with expertise in data science or who can communicate with both technical and business stakeholders.

The financial system needs to be democratised via AI, and this demands strong, human-centred leadership that is willing to spend money on talent and technology. Unless there is a built AI strategy, advancing past the experimental stage is strenuous.

The majority of financial services need a centralised data foundation that enables analysis and wise recommendations. To enable speed and agility, a new operating model that does away with functional silos is to be implemented.

For customers and SMBs, artificial intelligence aid in redefining and restoring foster trust by providing their clients with personalised experiences. Selling or promoting AI adoption to the business is essential for AI success in the financial services industry.

Individuals frequently believe that a good AI model can handle any issue. The model, however, only contributes five per cent to the solution. The remaining 95 per cent consists of integration, instrumentation, validation, continuous monitoring, and dollarisation. In financial services, explainability triumphs over model performance. Banks and other institutions must strike a balance between the urge to innovate and deploy cutting-edge AI and the realistic regulatory requirements surrounding explainability, robustness, and fairness in particularly sensitive areas like credit underwriting.

To involve data engineering, implementation engineering, operations, support, and frequently to establish lasting success, a comprehensive teaming model centred on cross-functional pods is essential. Leveraging AI and ML to create experiences and generate commercial value requires an apparent business strategy, a good go-to-market strategy, and an operational plan.