Advanced Computing in the Age of AI | Wednesday, June 16, 2021

Feedzai Gets Another $200M to Further Expand its Financial Risk AI/ML Management Platform 

Financial crime prevention and risk management vendor Feedzai has secured another $200 million in Series D investment money as the company works to expand its sales footprint, product offerings and partner strategy around the globe.

Feedzai, which is based in San Mateo, Calif., and Lisbon, Portugal, received the latest funds from global investment firm KKR, with participation from existing investors Sapphire Ventures and Citi Ventures, according to a March 24 (Wednesday) announcement. With the latest funding round, the company has now raised more than $1 billion from investors to pursue its strategy.

The Feedzai platform has a wide range of customers around the world among financial institutions, payment providers and merchants who use the platform to manage their risk of financial crime, according to the company. Feedzai's cloud-based platform uses AI and machine learning tools to process customer and third-party data to identify, assess and accelerate the remediation of potential threats. Feedzai’s platform leverages machine learning to learn from past behavior and augments financial crime detection by capturing schemes undetectable to the human eye.

A company spokesperson could not be reached for comment despite several attempts by EnterpriseAI.

This is the second notable announcement from the company this month. Earlier in March, it unveiled a new AI fairness framework called Feedzai Fairband, which uses an AutoML algorithm that automatically discovers machine learning models that have less bias. This process is accomplished with zero additional model training cost while increasing model fairness by an average of 93%, according to the company. Fairband is designed to help financial institutions around the world make better and fairer decisions when assessing customers for loans and other transactions.

Feedzai Fairband can be used with any fairness metrics, model metrics, and sensitive attributes such as age, gender, ethnicity, location, and more. It is designed to work with any algorithm and model settings.

“Feedzai Fairband is one of the biggest milestones in the financial services industry as it presents a low-cost, no-friction framework to address one the biggest problems of our era – AI bias,” Dr. Pedro Bizarro, the company’s chief science officer, said in a statement. “By creating the most advanced framework for AI fairness, Feedzai is allowing financial institutions to incorporate a critical piece of technology that addresses a problem under close public scrutiny with proven damaging effects across the globe. Building accurate and fairer models will be less challenging from now on.”

Steven D'Alfonso, IDC analyst

Steven D’Alfonso, a financial compliance, fraud and risk analytics analyst with IDC, said Feedzai’s latest funding is noteworthy.

“There is a lot of competition in the market for AI-based financial crime prevention and detection solutions,” said D’Alfonso. “Feedzai has been a fast-moving company, built from the ground up, based on AI while some of their larger competitors may have needed to build-out AI capabilities or develop new platforms.”

The additional $200 million investment from its latest funding round could help change that, he added.

“The playing field for fraud management applications is quite narrow today,” said D’Alfonso. “I don’t see vast differences in capabilities between many players in the fraud space. Investments such as this will allow Feedzai to continue to invest in its AI capabilities and also broaden their offerings.”

Overall, the financial fraud management marketplace is interesting, with financial institutions looking for AI-based offerings that operate within a framework of trustworthy and ethical AI, he said. “Fairness or bias is one element of that trust framework. The other elements, which are just as important as fairness are explainability, robustness/safety and lineage/traceability of data.”

D’Alfonso said he sees the bias issue and discussion around it as particularly pertinent topics today.

“The weight of its importance is somewhat dependent on how AI is applied within the bank,” he said. “It can be argued that bias is a much more important factor in credit underwriting decisions or using customer analytics to devise individualized product offerings than perhaps it is fraud detection. But certainly, bias in fraud detection algorithms can pose problems that can result in poor customer experiences or worse, an event that brings about some negative media attention.”

That could be seen in cases such as a biased credit card fraud detection algorithm that blocks transactions of a certain class of individuals at a higher rate than everyone else, said D’Alfonso. “I could see that type of situation causing reputational issues for a bank and potential regulatory scrutiny,” he said.

Preventing such biases is critical today for financial institutions, he added.

“I believe all providers of AI applications should have transparent bias assessments to help end users ensure that their algorithms are operating in a fair and expected way,” he said.

 

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