A CFO perspective: AI and the financial services industry

Tom Berquist’s 13 years experience in private equity helps him align his management team at TIBCO Software (TIBCO) with investors to ensure that the right decisions are made when allocating capital to product development, sales, marketing or services.

Despite extensive qualifications and experience in marketing and accounting, Tom regularly finds himself working in the technology industry. This is intentional, because of the vibrancy of the environment.

He said, “You never stop learning in the technology industry. Constant innovation is a regular occurrence and creates an extremely fast-paced workday. The vibrant character of this industry generates a chance landscape when it comes to improvement – making this industry mainly encouraging the individuals who never stop learning. While some of the industries place significant weight on seniority/superiority in defining employee development, the tech industry places a quality on fast-thinking, rigorous analytical thought, and reliable results.”

In an email interview with Enterprise IT News, Tom shared how TIBCO’s solutions are overall extremely useful for CFOs and how he; a CFO himself; is leveraging what TIBCO has to offer the financial services industry.

EITN: Can you briefly share about what you use TIBCO Solutions for?

Tom: TIBCO is a large enterprise software provider with an extremely useful technology solution.

In my role as CFO, I use many of our products for connecting our applications, analysing our transaction data, and communicating our financial metrics to our investors. This gives me the opportunity to test new products before they are released to our customers.

EITN: What is your opinion about the use of artificial intelligence (AI) when it comes to preventing cybercrime like money-laundering, fraud, and so on?

Tom: AI already affects compliance activities. AI has the strength to learn and decode outlines.This enables machines to be managed by training them to learn how to maximise performance in certain events. AI is easier, more efficient and more intelligent. Since AI is an intelligence-based, it can detect real risks instead of false alerts, allowing for a quicker risk investigation.

In addition to helping financial institutions reduce threats by more efficiently dealing with the regulations, they could cut the costs of the task – mainly by minimising false positives in monitoring systems and refocusing human expert resources on other, more efficient, areas of suspicious activity.

Therefore, a broad collaboration between financial institutions and AI utilisation could effectively stop criminals from trying to launder their money in banks. If we use the most talented staff to turn transform skills into AI, this is an extremely effective safeguard against the bank’s fraudulent cash flows.

Cognitive computing based on AI is much more effective than hundreds of compliance officers in stopping money laundering. It is just a matter of companies’ readiness to start using new technology.

I think one of the key aspects was around the prevention of fraud before it happens.

Whether it is money-laundering, trade surveillance, or credit card or insurance fraud, data analytics provides that visibility and enables the finance roles to identify, prevent, and act on fraudulent transactions quicker.

The other top function of AI and data analytics, would be understanding the customer – how can we maximise the customer experience, and what do they really need to provide visibility on potential business opportunities within the organisation.

EITN: I understand that TIBCO has strong roots in the finance sector with an illustrious history on Wall Street This has expanded into enabling other things for the trading community and other industries.  Can you share about the use of AI and data analytics in finance roles you have held before, or its use in financial institutions you have worked at before like Vector Capital, Citigroup and so on?

Tom: Anomaly detection is one of the best use cases for AI in financial services. There are huge volumes of data being managed by these organizations and it’s impossible to examine every transaction for fraud, mistakes, or looming problems.

AI helps identify patterns in the data that help find fraudulent mortgage requests, reduce claims fraud, identify duplicate transactions, isolate transactions with missing data, and improve underwriting processes.

EITN: Gut instinct versus analytics – your previous role at Vector and more, may have required you to place big bets, and there was a level of uncertainty and risk involved. With analytics having progressed the way it has, would you rely 100% on analytics, right now? If yes or no, why?

Tom: AI has been a success in fighting financial fraud for a number of years now— and the future looks brighter each year, as machine learning catches up. AI is exclusively effective at stopping fraud against credit cards, as e-commerce and online transactions have expanded rapidly in recent years.

Fraud detection systems monitor locations, actions and purchasing patterns of customers and activate a security mechanism when something seems out of order or opposes the spending pattern that has been developed. Banks also utilise AI to disclose and avoid another kind of financial crime, which is money laundering. Machines identify suspicious activity and help reduce the costs of research into the alleged money-laundering schemes.

Predictions in the future of AI applications in financial services is a happening topic nowadays, but one thing is certain that AI has been very quickly reshaping the financial industries’ market landscape.