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AI

AI-powered fraud-fighting platform Resistant AI raises $16.6M

Resistant AI, a startup developing automation technologies that respond to vulnerabilities in financial services, today announced that it raised $16.6 million in a series A funding round from GV (formerly Google Ventures) with participation from Index Ventures, Credo Ventures, and Seedcamp. The Prague, Czech Republic-based company says it’ll use the funding to build out Resistant AI’s product, engineering, and sales operations teams beyond their current offices in Prague, London, and New York.

Financial fraud is on the rise as business shifts to digital during the pandemic. For example, according to the Aite Group, 47% of Americans experienced financial identity theft in 2020. TransUnion found that the percentage of suspected financial services digital fraud attempts increased 149% when comparing the last four months of 2020 and the first four months of 2021. And 74% of organizations were targets of payment scams in 2020, AFP reports in a recent survey.

Founded by the team behind Cognitive Security, an AI-powered cyberthreat detection platform that Cisco acquired in 2013, Resistant AI claims to add an “extra layer” to existing checks for financial apps and services. Leveraging a combination of statistical modeling and machine learning, the company’s product categorizes and prioritizes false alerts to uncover fraud techniques by spotting relationships among seemingly unrelated transactions.

“After leaving Cisco, we have decided that the rapid fintech expansion and overall automation of financial services created new risks — and dealing with new risks is something we really excel at and enjoy as a team. So we have started Resistant AI in early 2019 to protect the AI systems used in the financial world,” CEO Martin Rehak told VentureBeat via email. “Most of the users and public seriously underestimate the volume of fraud and financial crime in digital financial services.”

Applying AI to fraud detection

Financial service organizations are increasingly embracing AI to streamline their operations — and reduce costs. A Deloitte survey reveals that 70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud. And in its research, McKinsey found that 25% of companies in the financial services sector have deployed AI technologies to support underwriting and risk management.

Resistant AI’s algorithms are designed to protect credit risk scoring models, payment systems, fraud, and customer onboarding systems. They can detect forged documents submitted to mislead or manipulate automated processes, the company claims, and increase the effectiveness of finding fake identities and “bust-out” fraud. Bust-out fraud is when a fraudster applies for a credit line, builds up a normal payment history, and then maxes it out without paying the balance.

For example, Resistant AI can verify invoices, payroll slips, bank statements, and more, protecting automated workflows from manipulation through forged documents in PDF and JPEG format received from third parties. The platform also applies adaptive security controls to defend AI systems and business from fraudsters while ostensibly minimizing customer friction.

“As part of Payoneer’s robust compliance program, we use Resistant AI’s technology in our KYC [Know Your Customer] process,” Karen Levy, COO of Payoneer, an early Resistant AI customer, said in a statement. “We greatly value the insights gained from it which help us validate the authenticity of documents submitted by customers.”

Forty-five-employee Resistant AI has a number of rivals in the AI-powered financial fraud-combatting space. Silent Eight offers technologies that avert fraud by learning how to conduct investigations from past alerts. There’s also Bleckwen, a cybersecurity firm developing fraud detection and prevention systems for banks and financial technology companies.

But Resistant AI — which has raised $19.35 million in total capital — says it already counts banks, insurance companies, and fintechs among its over 30 customers, including KBC, Habito, and Twisto. They’re processing tens of millions of transactions per month on the platform; Rehak expects Resistant AI to hit $1 million in annual recurring revenue by the end of the year.

“We have grown approximately 10 times in the last 12 months and expect the rapid growth to continue,” Rehak said. “Inscribe is probably the closest competitor, competing against our document forgery detection product, and some new companies are appearing. For example, PayPal-backed Sensity is not competing directly with anything we do, but are serving the identity verification vertical, as they protect the onboarding processes from deepfakes … But the shift to digital channels [has] led to increased automation and the overall impact — in the narrow domain of our business — has been positive.”

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Categories
AI

Forrester: Adopting fraud-fighting AI requires the right technical framework

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AI tools have become part and parcel of fraud management solutions in the enterprise, according to a new Forrester report. In it, analysts at the firm identify key fraud management use cases where AI can help, mapping how brands can deploy AI technologies in each scenario.

Fraud is on the rise worldwide, and it’s impacting enterprises’ bottom lines. According to a PricewaterhouseCoopers survey, 47% of businesses experienced fraud in 2019 and 2020, which cost a collective $42 billion. Some studies show that the pandemic is playing an increasing role. In a report commissioned by J.P. Morgan, nearly two-thirds of treasury and finance professionals blamed the pandemic or the uptick in payment fraud at their companies.

The Forrester report outlines AI’s unique strengths in combating fraud, including its ability to boost monitoring accuracy, augment human intelligence, and bring biometrics into the mainstream. Feeding rich datasets into fraud detection AI models can help spot fraud that rules-based systems overlook, Forrester notes, while those same models can be used by security teams to prioritize which alerts to investigate. Meanwhile, emerging AI-based solutions like biometric verification enable app authentication in near real time.

The report stresses, however, that organizations need to have the right technical pieces in place to reap the benefits of fraud-detecting AI. For example, prediction models and risk scoring systems must be low-latency to cope with large transaction volumes. Beyond this, data scientists must build training, test, and validation datasets, which can be challenging. According to Forrester, many banks report that their training data is miscategorized and suffers from quality issues like missing fields or inconsistent spellings of people and organizations. And in Asia, banks are reluctant to provide training data to consulting firms — and sometimes even to employees.

Indeed, other reports show that data issues plague companies of all sizes on their AI journeys. In an Atlation survey, a clear majority of employees (87%) pegged data quality issues as the reason their organizations failed to successfully implement AI and machine learning. McKinsey similarly estimates that companies may be squandering as much as 70% of their data-cleansing efforts.

Overcoming challenges

Organizations must also ensure that models evolve over time and explain why certain transactions are deemed fraudulent, Forrester says. They also need iterative, closed-loop workflows to train models and integrate them with data sources like blacklists, whitelists, device IDs and reputations, and know-your-customer lists.

The report recommends that companies use a combination of on-premises and cloud-based implementation options for their AI fraud detection use cases. While on-premises solutions offer convenience, the cloud has the potential to improve training and inferencing performance, Forrester notes, lowering costs and in some cases providing enhanced protection.

“As more fraud management vendors offer cloud-based solutions, it makes sense not to invest solely in static on-premises hardware and software, but to augment their on-premises assets with elastic computational resources in infrastructure-as-a-service or software-as-a-service solutions,” coauthors and analysts Andras Cser, Danny Mu, and Meng Liu wrote.

Once barriers to the adoption of fraud-fighting AI are overcome, the advantages can be enormous. Visa prevents $25 billion in annual fraud, thanks to the AI it developed, SVP and global head of data Melissa McSherry revealed at VentureBeat’s Transform 2021 conference. At a higher level, aggregate potential cost savings for banks from AI applications has been estimated at $447 billion by 2023.

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Repost: Original Source and Author Link