Martini.ai Proposes Industrywide Automation Standards for FIs

Artificial intelligence (AI)-powered credit intelligence platform martini.ai has proposed industrywide automation standards designed to provide a common language for describing the steps in the evolution of credit intelligence in the financial services industry.

With this Financial Autonomy Ladder framework, financial institutions will be better able to adopt advanced risk management capabilities and benchmark their progress, the company said in a Tuesday (Aug. 26) press release.

Martini.ai aims to make this framework a recognized standard by working with industry associations, regulators and technology providers, according to the release.

The solution is modeled on SAE International’s autonomy standard used by the automotive industry, which has helped that industry set goals and communicate progress, martini.ai CEO Rajiv Bhat said in the release.

“We’re not trying to own this — we want the entire industry to benefit from having clear, standardized terminology for automation capabilities,” Bhat said.

The Financial Autonomy Ladder includes six levels: no AI involvement; AI producing signals from data while human produces reports and decisions; AI producing signals and reports while human makes decisions; AI producing signals and reports and recommending decisions while human reviews decisions; AI making decisions while human provides oversight for complex cases; and AI making decisions and strategies, per the release.

While no institution operates at the top level today, that level provides a vision of the future of “self-optimizing financial infrastructure,” the release said.

“The Financial Autonomy Ladder gives [institutions] the language and framework to understand where they are and what it takes to reach the next level,” Bhat said. “The institutions that embrace this evolution soonest will have decisive advantages as markets become increasingly dynamic and interconnected.”

Martini.ai’s AI-powered model, which the company refers to as credit risk assessment interpolation, ingests market data, runs it through graph neural networks and produces real-time risk signals based on how one company’s tremor could become another’s tailspin, PYMNTS reported in July.

“Instead of spending time understanding the risk, now teams can spend time addressing the risk,” Bhat told PYMNTS CEO Karen Webster in an interview posted July 10. “The future belongs to companies that act faster, not those who analyze more.”