Why banks are adopting AI platforms as a service
Sudhir Jha, senior vice president and head of Mastercard’s Brighterion unit, told Karen Webster during the latest agenda discussion that artificial intelligence (AI) can strengthen credit and risk management and expand its value far beyond simply improving day-to-day operations. .
But to get there, companies need a little support.
“What was cutting edge technology five years ago is no longer cutting edge,” he said, and companies trying to keep up with rapidly changing science and data analysis by them- same can be quickly overtaken. A business that starts out with regression and model analysis solutions can scale quickly and take advantage of neural networks.
For banks, acquirers and healthcare payments managers, he said, using vendor AI-based solutions helps prevent undue losses due to fraud, abuse and misallocation of funds and poor underwriting decisions.
Jha’s comments came against a backdrop where 88% of financial institutions (FIs) said the pandemic had made lending and lending more difficult. Jha said the pandemic has underscored that businesses need anti-fraud systems in place that are adaptable and change as scammers change.
“Every month, even every day can be different,” he said, adding that “as a business there is not a lot that you can react to.”
While most leaders see the value of deploying AI systems in their risk management efforts, many leaders are unsure of how to go about making this aspiration a reality. Tech talent in AI is hard to find and takes years to develop, and building models and solutions in-house can take years.
Given these challenges, it makes sense to work with outsourced vendors (Brighterion among them), where tapping into what Jha called a fully prepared solution cannot be different from how purchasing an HR solution it is. ten years ago could have been.
We are moving towards turnkey solutions where AI modeling is integrated, with the different and disparate data elements that businesses need to strengthen their own defenses against fraud.
“Your own data is going into the system, and as the model trains on it, you’ll get more and more value – and you can get started pretty quickly,” he said. “This is the next evolution of AI solutions. “
Along the way, quick and accurate decision-making empowers FIs (and other businesses) to create new lending products and services, as evidenced by the explosion of buy now, pay later (BNPL) offers. in the past year only among traditional FIs and digital-only FinTechs. , he said.
Improve credit risk management
Jha noted that AI has particular value in managing credit risk. It can leverage real-time data to help lenders make better early decisions and spot potential setbacks and fraud before losses affect bottom line.
There is a bit of imbalanced adherence to AI, as 79% of banks with over $ 100 billion in assets use AI, but only a fraction of small banks do. And while progress has been made, the opportunity to create new spaces is significant. In 2018, 5% of FIs reported using AI systems in areas such as credit risk management and fraud detection. By 2021, that figure had tripled to 16%.
Jha argued that small businesses might not have enough data (especially when applying for initial credit) to create AI models. Even on the delinquency side of the equation, there is not enough data available to be predictive. He said the platform models have broader options that corporate clients can access to create the best models possible.
The urgency to exploit the platform model is there, as 93% of all acquirers said they were seeing more fraudulent transactions than a year ago. Ninety-eight percent of all acquirers who use AI use it to detect fraud, and 79% of them see AI as the most important tool in their fight against fraud .
“Everyone has a hard time onboarding the hundreds and thousands of small businesses they want to do business with,” he said.
The approval dilemma
Acquirers face a dilemma when battling fraud, Jha said. On the one hand, they want to ensure that fraud is stopped early, before these suspicious transactions are submitted to issuers. But they also want to increase approval rates – in Jha’s words, “because the issuer knows you’re a good acquirer.”
But acquirers should also be concerned about the risk of integrating fraudulent merchants. Machine learning (ML) and AI technologies can speed up the process. He told Webster that Brighterion is bringing its data assets together to create a data integration solution that can assess credit risk at that initial point of contact.
“You make the system more secure,” he said, because every transaction and every customer is viewed holistically.
Looking ahead, Jha said we will see a continued evolution of companies that continue to invest in AI by creating solutions from the ground up.
“You’re not really going to differentiate yourself by doing this,” he said. “If you can actually have a vendor who can provide you with a turnkey solution and will invest in innovation in the future, you’d be much better off just adopting it and then making custom improvements to that. “
As he told Webster, in efforts to tackle credit risk and fraud, “adaptability has become the name of the game.”