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Opinion > Melissa Global Intelligence

Step back from the hype: what can AI realistically deliver in 2020?

Barley Laing, UK managing director at Melissa Global Intelligence | 15:45 Friday 17th January 2020

While talk of artificial intelligence (AI) in financial services generates great excitement, it’s important not to get too carried away and caught up in overly ambitious uses of the technology.

Instead, banks should look at the reality of what’s possible with AI today. By taking a smart approach that doesn’t necessarily require a comprehensive commitment, banks can leverage AI to deliver efficiencies and value at low cost and with minimal risk.

Originally developed to support advanced scientific research, AI is already fuelling key operations in banking. Areas where it’s demonstrating the greatest benefits are in improving data quality, the identity verification process, and in delivering ‘informed’ decision making on the products and services offered to customers – — all of which are being driven by machine- reasoning AI.

One standout form of AI, semantic technology — which has already proven its worth in the healthcare and pharmaceutical industries — is leading the way in the financial services sector. Semantic technology, or semtech, associates words with meanings and recognises the relationships between them. This makes it possible to apply context and make inferences with data, ensuring properly validated identities, as well as broader data quality and integrity. 

An important part of semtech is the machine reasoning and automated pattern recognition it provides, in real -time, to identify possible fraudulent applications. As a result, semtech supports a more seamless customer onboarding experience, helping to deliver a clear competitive advantage. It’s by adopting such AI-enabled data quality and identity verification technologies that integrate with existing banking software platforms, that financial institutions of any size can ensure they are KYC and AML compliant. 

Additionally, semtech can enable banks to gain in-depth intelligence on their existing customers by making powerful, real-time connections between the data in their records. Then, by applying 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.

Semantically enabled technology not only addresses the core issues in data quality and data completeness faced by financial institutions, it can also provide vital real-time decision making around ID verification, speeding up customer onboarding as well as providing the bank with that bigger picture to derive better understanding around which additional products and services customers may be interested in. Overall, errors are reduced and insights from the data are more sophisticated and quickly generated, freeing banking staff to focus on other important areas, such as providing a standout customer experience and developing innovative new products.

In all the noise surrounding artificial intelligence, it’s still possible for banks to benefit from AI without succumbing to the hype and related risks of an all-in effort, and instead hit the ‘sweet spot’ with semantically powered solutions that are proven to increase efficiency at low cost.

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