Commercial Banks

Everything you need to assess the attractiveness of the opportunity.

• Build a stronger pipeline with fewer surprises: qualify prospects using forward‑looking indicators of customer health so you can adjust terms early, reduce exposure, and avoid unexpected write‑offs as conditions shift.
• Move faster from analysis to decision: accelerate spreading and scoring with AI‑enabled financial data extraction and credit modeling, speeding up credit decisions without adding risk.
• Standardize decision-making and strengthen governance: apply and validate consistent internal credit frameworks aligned to S&P Global Ratings methodologies, supporting defensible decisions across teams and deals.
• Bring macro and sector risk into every decision: integrate timely macroeconomic, political, and sector signals (including stress scenarios) into borrower and portfolio analysis to detect risk earlier and reduce surprises.
• Improve approvals and post‑close oversight: standardize credit committee documentation with comparable credit metrics and independent valuations, then monitor continuously with alerts, migration signals, and watchlist updates for earlier intervention and lower losses.

Credit models and data to support your own risk modeling, or models & methodologies to strengthen approaches.

• See portfolio risk clearly, in one place: centralize borrower, sector, and economic data into a unified portfolio view to monitor exposures and emerging risks with confidence.
• Shift from reactive to proactive risk management: use credit deterioration alerts, higher‑PD flags, and macro alerts to detect deterioration earlier and act faster.
• Quantify concentration and downside risk: estimate how borrower and sector credit metrics change under different economic conditions to better understand concentration risk.
• Make stress testing and capital decisions more defensible: use macro/industry scenarios and portfolio loss & capital‑ratio modeling under defined stresses to improve scenario credibility and strengthen capital planning decisions.
• Improve recovery outcomes on problem assets: benchmark expected loss with historical default and recovery data, supported by independent pricing for distressed assets to guide write‑downs and sale decisions.

Robust data across risk and pricing to meet a range of regulatory needs, in a simplified manner.

• Connect scenarios to regulatory outcomes: link macroeconomic and industry scenarios to portfolio capital and loss modeling to support CECL, IFRS 9, and Basel III requirements, improving clarity on capital impact.
• Improve risk consistency across portfolios: apply consistent PD/LGD and scorecard models across rated and unrated names, supported by long‑run default history.
• Increase confidence in fair value and market reporting: use independent prices, spreads, and valuations for transparent mark‑to‑market and liquidity reporting.
• Reduce manual effort and strengthen audit readiness: automate capital, liquidity, and stress‑test calculations with transparent data lineage, and centralize source‑of‑truth datasets to support audit trails and regulator queries.
• Enhance oversight and compliance monitoring: track macro/sector indicators, credit migration, and emerging risks and support compliance needs such as sanctions “look‑through,” margin/collateral rules, and ESG disclosure frameworks.