AI for Banking
Use the power of enterprise-scale AI to improve operational efficiency, increase customer engagement, and mitigate risk.
The banking industry is struggling to respond to today’s challenges and opportunities with traditional IT systems
Banks cannot flexibly and rapidly respond to these market dynamics due to rigid IT systems and disparate, segregated data sources. Historically, efforts to integrate enterprise-wide data, build analytics, and deploy insights across the organization have proven expensive and difficult to maintain.
The C3 AI Suite enables banks to rapidly build AI applications on top of legacy and cloud infrastructure
The C3 AI Suite provides the comprehensive services to build enterprise-scale AI applications 25x to 100x faster than alternative methods. The C3 AI Suite enables organizations to use all relevant data sources that underpin machine learning models to rapidly generate predictive insights, enhance historical rules-based banking systems, improve critical compliance and operational processes, and transform customer experiences. A recent study of the value chains of three major global banks demonstrated that the annual economic value of the C3 AI Suite deployed across the enterprise could exceed $100 million.
C3.ai Applications for Banking
C3 Anti-Money Laundering
Reduce regulatory exposure, risk, and investigation costs by accurately identifying, prioritizing, and reporting suspicious activity. C3 Anti-Money Laundering uses supervised machine learning algorithms to analyze data from multiple systems to increase accuracy rates in flagging suspicious activity while reducing the number of false positives.Learn More
Banking Use Cases Addressed by the C3 AI Suite
Intraday Liquidity Management
Identify globally optimal liquidity positions in real time to satisfy payment and settlement obligations, and regulatory requirements. Machine learning-based stochastic optimization techniques use real-time data for agile liquidity management.
Credit Approval Process Optimization
Reduce exposure to credit risk and loss through early identification of conditions that may affect consumer or commercial risk profiles. Maintain a comprehensive data view to accurately assess risk while streamlining credit approval.
Detect suspicious trading behavior indicative of market misconduct. Advanced machine learning algorithms correlate all trading-relevant structured and unstructured data and drive contextual analysis to enforce policy compliance.
Securities Lending Optimization
Prioritize securities locate requests by expected availability, likelihood of fulfillment and potential profitability. AI algorithms also predict likelihood of lender recall events for every security-lender combination and learn from recall events to make better predictions over time.
Customer Profitability Optimization
Improve profitability of customer mix, lower customer attrition rate, and increase efficiency of retention activities. Supervised machine learning models analyze customer attributes, behavior, and external factors to determine churn risk and the most effective intervention for each customer.