Financial industry giants unveil AI challenges

NEW YORK — From credit cards to title insurance to lending and even fraud and risk management, the financial industry is using AI tools and technologies. But implementing these tools and solving their inherent problems has proven difficult for financial institutions.

This was the sentiment shared by major credit card providers, insurance companies and banks at the Ai4 Financial Summit 2022 here on March 1. The application of AI tools in finance is necessary for many institutions, and many plan to increase their budget to continue implementing the tools.

According to a November 2021 report from Enterprise Strategy Group, 65% of 706 senior IT professionals in the financial industry plan to increase their IT budget in 2022.

Among this increase, 62% of respondents say they are likely to increase their spending on artificial intelligence and machine learning.

However, as finance involves sensitive consumer and big business data, some companies find themselves trying to balance the benefits and risks of AI tools.

The number one problem for companies using AI technologies in finance is lack of education, said Priya Rajan, CMO at DataVisor, during a panel discussion on AI and credit cards at the conference. .

DataVisor, a fraud and financial crime detection company, uses AI and machine learning to identify fraudulent attacks. But, Rajan said, so much is still unknown about AI that it’s hard to identify what is real AI and what isn’t.

“Education is such an important part of this transformation in this industry and I expect that to continue over the next decade as we are only scratching the surface of what technology is and what she can do,” Rajan said.

The challenges of AI in finance

Another challenge is explainability, said Clay Jackson, vice president of product management for small business cards at Capital One, during the roundtable.

Credit card providers are placed in a tricky situation where not extending someone more credit can mean that person can’t pay funeral expenses or find a job.

“We are taking actions that impact the lives of customers,” Jackson said.

When a customer is denied credit, institutions need to be sure why they are saying no, Jackson said.

“I feel like the explainability issue is slowing us down,” he said. “And for the right reasons.”

Artificial intelligence tools also pose privacy and bias issues for the credit card industry, said Rick Ballmann, vice president of engineering, data intelligence and customer experience at American Express. , during the same round table.

While banks and financial institutions can offer loans to consumers based on where they’re currently spending their money, that doesn’t mean they should, Ballmann said.

“It’s like knowing the line between going too far and what the customer would appreciate as good customer service,” he continued. “That’s not exactly what’s possible, [but] what is the right thing to do for the customer.”

Using AI tools in fraud detection also presents data privacy Questionssaid Besa Abrashi, senior project manager at American International Group (AIG), a finance and insurance company.

As a member of AIG’s fraud detection team, she said there are different data privacy regulations and procedures that must be followed when exchanging data from one location to another – onsite, offsite or in the cloud.

“You have to go through all these data privacy regulations and procedures to get all the approvals,” Abrashi said.

To be able to stay competitive in the game, you have no choice but to grow your AI lab space within your organization.

michelle wangChief Managing Officer, Wells Fargo

AI software vendors need to be flexible about where they install their AI platforms to ensure that financial institutions can avoid data privacy issues, Abrashi continued.

“Especially now with all the regulations that are getting stricter and stricter in terms of what kind of data you can share,” she said. “Everything now is PII [personally identifiable information]. Not only first and last name, but they consider all PII.”

In the reinsurance industry, AI tools have the potential to solve many problems, but their integration is the problem, said Dean Marcus, actuary at Guy Carpenter & Company, a New York-based reinsurance firm.

“Partly because of the data challenges…but also just because of calibrating the models and ensuring they do what you want them to do in a timely manner,” Marcus said.

Despite all these challenges, financial institutions have no choice but to use AI tools to stay competitive, said Michelle Wang, chief management officer at Wells Fargo.

Wang works in Wells Fargo’s risk management department, which uses artificial intelligence technologies for things like data analysis, she said.

“As new users, it’s always difficult to figure out how to use the tools that are available to us,” she said. “To be able to stay competitive in the game, you have no choice but to grow your AI lab space within your organization.”

Enterprise Strategy Group is a division of TechTarget.

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