How Artificial Intelligence is Reshaping Financial Services

Identifying AI Use Cases in FinServ in an Experience-First World

Artificial Intelligence (AI) continues to gain traction as a significant business driver for large organizations across major industries. The traditional financial services industry was known to be slower in adopting new technologies, with some organizations using software running on COBOL or Fortran, which was invented in the 50s and 60s. Recently, thanks to the rise of fintech and more AI applications becoming available, the industry is starting to accelerate investment in AI across all key business functions. But how should the right AI use cases in FinServ be identified? While internal efficiency use cases might be great short-term wins, zeroing in on the customer experience may prove to be a competitive advantage in the future.

The Race to Digitalization in Finance

ai use cases in finserv

Financial services were undergoing a profound transformation – even before bringing AI into the mix – with the move to a digital economy. Within financial service institutions, core business functions are often last to get updates, especially if they’ve been running smoothly for years.

Regardless, consumers now expect services like banking, insurance, and investment to be available online. Rapid digitalization of these services has been a challenge for many financial service institutions who may not have viewed themselves in the technology space. Yet keeping pace with customer demands and finding opportunities to attract and retain customers is rewarding.

In finance today, success revolves around data, and there are increasingly fewer products that have a physical component to them. This and the need to quickly and accurately process data is what makes the entire industry ripe with opportunities for AI. As companies look to capitalize on AI, demand for AI talent has become competitive. MMC Ventures notes that technology and financial service companies are currently absorbing 60% of the AI talent pool.

To continue to stand out from competitors and be set up to evolve at a faster pace in the future, providers should consider strategic AI use cases in FinServ that can help change market perception, provide value to customers, and improve productivity – leveraging vendors to bridge talent gaps and scale.

Where AI and Customer Experience Converge

The scope of potential use cases for AI and ML in finance is massive. In thinking about fintech, we see opportunities in core product applications, accounting, payments, and more. Within the banking and investing sphere, AI is already being used for chatbots and fraud detection. Insurance providers are investing in AI solutions that support claims management, policy management, and more.

And while business use cases are becoming more varied across fintech, banking, investing, and insurance, consumer experience-centric applications – like personalized journeys, credit application, claims management, smarter chatbots, agent assistants – appear to be the most common and successful to deploy at scale. To do this, companies often have to work with multiple vendors and applications to collect, label, prepare, and converge all that data to train their AI models effectively and deploy the models in production.

However, there are a number of challenges that come with developing AI in financial services. Similarly to government or medical applications, they often involve leveraging data that is mixed with confidential or personally-identifying information (PII). Naturally, companies looking to implement AI need to find a solution that can create accurate training data while accommodating security needs, verified by humans at scale. This can make finding the right partner support difficult, especially in restrictive geographies with very specific PII rules.

Fortunately, companies can now utilize data partners that can ensure data stays local, offer private cloud and on-premises services to ensure compliance and control of how the data is utilized, have additional protocols in place, like secure digital workspaces, and are GDPR, CCPA, and ISO-certified.

Investing in AI and finding a data collection and annotation partner that supports enhanced security protocols will be critical to achieving success in financial services as the demand for more personalized services grows.

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