Automated financial analysis: from Excel to AI scoring

Introduction

For years, financial analysis relied on Excel spreadsheets and manual models. While flexible, this approach no longer scales in a world of real-time data, regulatory demands and rising risk.

In 2025, leading fintechs, insurers, brokers, banks and enterprises are moving to automated financial analysis and AI-based scoring. This article outlines the evolution from spreadsheets to real-time scoring, the benefits of automation, key use cases and the checklist for selecting a modern solution.

1) The Excel era: flexibility with fragility

Excel empowered analysts to build ratios, run scenarios, and tailor models. But:

• Error-prone (manual entry, broken formulas).

• Not scalable (hard to apply to 1,000+ entities).

• No standard explainability (depends on the analyst).

• Slow and costly → poor productivity.

2) Traditional financial analysis software

Many vendors built software to standardise ratios and consolidate data. Useful, but:

• Often rigid, slow to update.

• Focused on historical data only.

• Rarely integrate real-time banking feeds or open banking APIs.

👉 Better than Excel, but not predictive.

3) AI-driven scoring and automation

API-first integration

• Connect banking, accounting, legal, public data.

• Sub-500ms latency for real-time scoring.

• Cuts manual effort, enables embedded flows.

Predictive models & weak signals

• Early detection of cash flow stress and payment deterioration.

• Trained on massive, multi-source datasets.

Explainability (XAI)

• Factor-level breakdowns and reason codes.

• Regulatory compliance (AI Act, auditability).

Inclusivity

• Reliable scoring for thin-file SMEs, startups, atypical sectors.

• Fewer unjust declines.

4) Use cases

• Bank: real-time SME loan application scoring with banking + legal data.

• Insurer: client risk evaluation during underwriting with alternative data.

• Broker: pre-scoring dossiers before submission.

• Fintech: embed API scoring in onboarding flows.

• Enterprise/ETI: supplier scoring to mitigate supply chain risk.

5) Selection checklist

• API-first with sandbox

• Multi-source coverage (banking, accounting, legal, public)

• Sub-500ms latency

• Explainability (XAI) with factor-level outputs

• Inclusivity for SMEs/startups

• GDPR + AI Act compliance

• Certified security (ISO, encryption)

Conclusion

Moving from Excel to AI scoring means moving from historical/manual to real-time/predictive/ explainable analysis.

👉 Organizations that adopt it reduce risk, improve efficiency, and deliver better client and partner experiences.

Next step? Try RocketFin and see how it can transform your processes : https://www.rocketfin.ai/demo