AI driven operational risk control ELEVATOR PITCH: Operational losses and fines (in the billions of dollars) continue to impact financial institutions and cause reputational damage. The legacy method to assess operational risk is subjective and based on a banks internal knowledge. This situation is strained YEAR FOUNDED: with increasing regulatory pressures. At acin we are transforming this to a modern data driven product – we have created 2018 standards and an AI driven network to enable peer comparison and quantification. Our vision is to make the banks and industry safer. TEAM SIZE: SECRET SAUCE: 70 Our product is based on a huge amount of research built from multiple tier 1 banks data. This has output, data standards, AI (refined on deep learning of our data), and a network of hundreds of thousands of connected data points. This is unique and would be very hard and expensive to replicate. TARGET CUSTOMERS: COO/CRO/Operational Risk USE CASES Control SMEs RELEVANT INDUSTRIES: 1 Enhance Engagement Between 1LoD and 2LoD Financial Services (Global A major European IB wanted to move to data driven engagement between the first and second lines of defence. Acin’s Markets, CIB, Retail, Asset platform processed over 2,000 controls in a matter of weeks to drive ongoing engagement with data. Management 2 Control Rationalization Major European retail bank had concerns over data quality. Acin’soptimization models identified compression opportunities of 71%, including high numbers of non-controls and duplicate controls. 3 Demonstrate Completeness to Regulators A US regulator challenged a GSIB to demonstrate the completeness of their controls environment. Acin’sdata was presented to the regulator as evidence of their leading position in the market. CLOUD WE WANT TO LEARN ABOUT: Insights you’d like form our partners e.g. • Identify larger community (e.g. data governance, change) in banks beyond our existing target user list. • Explorehow/if our data standards should become open and publicly available for wider adoption. • Identify other opportunities across wider FI space to use the acin product. rd • Identify complementary product/data from 3 parties to further the acinoffering.