June 27

Trade finance often still involves paper documents moving between banks, shippers, and exporters, and JPMorgan just gave a vote of confidence to Cleareye.ai to disrupt that archaic process.  

JPMorgan’s Trade and Working Capital group has made a strategic investment in Cleareye.ai, a startup that it started working with last year to digitize and automate trade finance, which has largely been stuck in the past.  

Through a combination of computer vision and natural-language processing, Cleareye.ai automatically ingests documents and then analyzes them to catch compliance and sanction violations or money laundering red flags.  

Instead of manually poring over documents, trade operations analysts can now skim Cleareye.ai’s reports, which drastically reduces processing times, human error, and risk. JPMorgan previously said the tool could “massively improve efficiency” for its commercial trade processing tool. 

Cleareye.ai and JPMorgan aren’t the only ones gunning to transform trade finance, though: Santander’s corporate and investment banking arm just made its own strategic equity investment in Komgo, which also aims to increase the efficiency, transparency, and security of trade finance.  

One of the superpowers of ChatGPT is its ability to simplify complex topics, which makes it an excellent educational tool for fledgling workers. SouthState is already seeing a significant productivity boost among its employees. 

ChatGPT is an intern’s new best friend, according to an executive at SouthState Bank who spoke at a recent American Banker conference. The bank trained an enterprise-version of ChatGPT on its own documents, allowing the bot to answer questions about internal information, complete with citations and references.

“As you learn about any new topic, it doesn’t make you an expert, but it takes a below-average person or an average person and ups their game,” exec Chris Nichols said on stage. “We’re just bringing on a bunch of interns this week for our summer intern program and we’re training them first and foremost on how to use our version of ChatGPT in order to quickly become experts at learning about deposits or regulation.” 

This aligns with a recent study that showed how an AI-powered customer service chatbot at a Fortune 500 firm was particularly useful for new agents, who benefited more from automated advice than their more experienced coworkers.  

“It normally takes an employee 12 to 15 minutes to figure out the correct answer,” Nichols said. “That gets reduced to seconds.” 

It cost SouthState about $50,000 to bring the tool to production and about $30,000 a month to test and run. It pays for itself, according to Nichols: “If you have 5,000 employees using it and they’re five to eight times more productive, that $30,000 a month is nothing.” 

For more examples of how GenAI is transforming financial services, read Insights Distilled’s recent report. 

Google Cloud wants banks to embrace artificial intelligence for flagging fraud.  

The company just released a new tool that does away with rules-based systems for AML compliance in favor of an AI-generated risk score based on a bank’s historical data.  

In a typical system, 95% of alerts for review turn out to be “false positives,” which waste time and resources for compliance teams. Google’s tool cuts down on the overall number of alerts that need human review, without letting risk management wane, thus increasing operational efficiency and effectiveness, according to the company.  

Since HSBC became an early tester of the product (it began using it in 2019), the tech has cut down the bank’s number of alerts by as much as 60%. It also now detects two to four times more “true positive” risk, or confirmed suspicious activity, Google said.  

By integrating Google’s tool into its customer monitoring framework, HSBC has been able to “improve the precision of our financial crime detection and reduce alert volumes, meaning less investigation time is spent chasing false leads,” according to head of compliance Jennifer Calvery. “We have also reduced the processing time required to analyze billions of transactions across millions of accounts from several weeks to a few days.” 

JPMorgan picked an executive with 30 years of experience at the bank for a prominent new role, highlighting the importance of context and relationships in building a successful AI strategy.  

The head of JPMorgan’s new data and analytics unit, Teresa Heitsenrether, will be responsible for infusing artificial intelligence into every facet of the bank’s business.  

“Using AI technologies effectively and responsibly to develop new products, drive customer engagement, improve productivity, and enhance risk management will be a top priority,” CEO Jamie Dimon and president Dan Pinto said in a memo announcing Heitsenrether’s hire. “Teresa is an outstanding leader with an exceptional track record, helping to build and transform some of our most successful businesses.”  

It’s telling that JPMorgan selected an internal leader with deep context and relationships across the firm to lead the new unit, instead of a hotshot external technologist.  

Meanwhile, as Distilled previously reported, none of the biggest banks in the United States have the same organizational arrangements for their AI efforts.  

For example, Citi funnels work through its AI center of excellence, while Bank of America organizes its AI initiatives along business lines, with chief experience officers from different divisions, including consumer banking (Teron Douglas) and wealth management (Christian Kitchell). CXOs report into BofA’s chief digital and chief marketing officer, David Tyrie.   

As payments and fraud prevention become more complex, artificial intelligence will play a key role in creating a frictionless, safe experience for consumers.

As financial products and services are increasingly embedded and delivered through new platforms, there’s more opportunity than ever for bad actors to exploit the system and commit fraud. But as the conversations and trends from Money 20/20 Europe made clear, artificial intelligence will be a key tool for FinServs to fight back and evolve their processes.

“Particularly with real-time and global payments, AML compliance and risk and fraud monitoring have a long way to go to deliver true frictionless experiences, while minimizing false positive and false negative flags,” according to an Insight Partners rep who attended the event. “This is one place where AI can be a true game-changer.”  

In addition to the Google example above, Insights Distilled has previously reported on how innovative tech firms are trying to use artificial intelligence to flag hidden patterns in payments data and automate continuous know-your-customer monitoring. The best fintech providers will be those that make orchestration easy across various data layers, Insight adds. 

The team also identified several other key narratives that emerged from Money 20/20 Europe: 

Disrupting B2B payments remains an untapped opportunity. Business-to-business transactions have been stuck in the past, making it a segment ripe for modernization. The fintechs that succeed will be those that take on specific geographies industries, or size cross-sections and solve the largest pain point within that niche.  

AI will change everything – eventually. Financial institutions are not standing still in the wave of GenAI, but regulatory and fairness risks remain – as well as questions on who should hold the burden of customer education. Service and support, product recommendation and delivery, risk scoring, payments optimization (particularly within FX and capital markets), and transaction processing and reconciliation are ripe for disruption, but timelines for production-level rollouts remain uncertain.