AI can 'replace the last 30 years of banking software'

AI technologies could replace or enhance every piece of banking software in use today, according to financial technology veteran and icon Thomas Siebel.

The former founder of Siebel Systems, now CEO of AI technology firm told delegates at Sibos 2021 that “AI represents an entire replacement market for everything we’ve done in enterprise banking software in the last three decades”.

As an example, Siebel cited CRM (Customer Relationship Management), a type of technology he did so much to develop and promote at Siebel Systems from the early 90s until its acquisition by Oracle in 2006.

Today, Siebel suggested, the global CRM market is probably worth about $1 trillion dollars, yet CRM does not offer end user companies a great deal of value in terms of helping to forecast revenues or market developments; and its capabilities can be undermined by human errors that devalue the data they hold.

By applying AI technologies to CRM, Siebel claimed, it would be possible to develop advanced predictive analytics capabilities.

He suggested AI could have a similarly transformative effect on supply chain management systems and AML compliance. “We can reduce the cost of AML compliance by, certainly half, maybe up to 80 per cent,” he claimed.

He also claimed that work has helped financial sector companies to deploy AI-based solutions able to generate results including a bank increasing daily trading in hard-to-borrow securities by 41 per cent - which Siebel said equated to an extra $14 billion worth of trading per day.

Also discussing use of AI at scale in financial services were Rafael Otero, chief product officer at Deutsche Bank; and Matthieu Vacarie, head of innovation and digital strategy for global transaction and payment services at Societe Generale.

Both listed use cases for AI within the two banks, including using machine learning and natural language processing (NLP), to draw data out of text and voice communications, in customer communication functions, to improve anti-fraud measures; and to enhance predictive analytics applications.

There was also discussion of work that both banks, and other technology firms are engaged in with SWIFT, on a new collaborative AI project.

A pre-recorded video featuring SWIFT CIO Tom Zschach summarised plans to extend a project that will focus initially on anomaly detection, to form the foundation for development of further AI-based services for SWIFT customers.

Vacarie outlined some of the advantages of banks working together using AI capabilities. For example, if banks share data – in an anonymised form – with each other and with trusted partners like SWIFT, anomaly and pattern detection can be completed at a much greater scale, enabling identification of fraudulent activity that could not be detected by a bank studying only its own data.

The panellists also stressed the need to work to ensure that machine learning models continue to avoid cultural or racial biases – and the need for some human input to avoid such biases having a negative impact on product or service delivery.

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