When the debate in financial services turns to artificial intelligence (AI), reality often gets lost in the excitement, with enthusiastic talk about over ambitious uses of the technology rather than what it can effectively deliver today.
By being smart with AI, and not attempting to get AI to ‘do it all’, it’s possible for the banking world to leverage AI to deliver efficiencies and value at low cost and with minimal risk.
The first thing banks have to recognise is that it’s AI technologies driven by machine reasoning that are set to have a significant impact on the financial services sector. Such AI technology can improve data quality, the identity verification process and
also deliver ‘informed’ decisions on the products and services offered to customers.
It’s a form of AI, semantic technology - which has proven its worth in the healthcare and pharmaceutical sectors, and has machine reasoning at its heart - which is set to have the biggest impact on the banking industry. Semantic technology, or semtech, associates
words with meanings and recognises the relationships between them. It’s this approach which enables it to apply context and make inferences with data, which helps to deliver properly validated identities as well as broader data quality and integrity.
In fact, semtech can, in real-time, identify possible fraudulent applications because of the machine reasoning and automated pattern recognition it provides. This enables those in financial services who integrate this technology with their existing banking
software platforms to deliver a seamless customer onboarding experience. Not only does it ensure that those banks that use it are KYC and AML compliant, but have a clear competitive advantage over those that don’t.
Another key benefit of semantic technology is its ability to enable financial services companies, of any size, to gain in-depth intelligence on their existing customers by making powerful, real-time connections between the data in their records. Using machine
reasoning built into the technology it’s possible to merge the missing pieces of customer data to support an informed decision about whether to provide a product, for example, a loan to a customer. Machine reasoning does this by helping to fill in any gaps
left by the customer as part of the application process or via other communications.
The benefits of semantic technology to the banking world are many. As well as addressing important issues in data quality and data completeness, the technology delivers critical real-time decision making around ID verification. This means it’s able to speed
up and improve the customer onboarding process. Additionally, banks gain big picture visibility on their customer base to derive better understanding around which additional products and services customers may be interested in. Best of all, with errors reduced
and more sophisticated insights quickly generated, banks are freed up to focus on other important areas, such as providing a standout customer experience and developing ground-breaking new products.
It’s time for those in financial services to ignore the hype around AI and instead hit the ‘sweet spot’ with semantically powered solutions that are proven to increase efficiency at low cost.