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SUS·Regulatory Engagements And Supreme Court Ruling Will Secure Resilience
1. Purpose To make the case for adopting AI at S&U. Goals: improve accuracy, reduce risk/cost, and enhance customer experience. Focus on underwriting, collections, compliance, and customer engagement. 2. Current AI at S&U Technology use now: robust data analytics, open banking integration in underwriting. AI initiatives: no clear public statements yet; limited direct AI activity. Awareness: sector evolving quickly; S&U likely monitoring developments. Conclusion: conservative but aware, recognising advanced data potential. 3. Proposed Program of Works AI in Underwriting & Credit Scoring: machine learning models + open banking data → better risk profiling. AI in Collections & Recovery: predictive analytics + robotic process automation (RPA) → improved recovery rates. AI in Compliance & Risk: natural language processing for regulatory monitoring + anomaly detection. AI in Customer Engagement: recommendation engines, sentiment analysis, chatbots. AI in Operations: automation of repetitive tasks via RPA. 4. Implementation Phasing: Phase 1 (0–6m): customer insights & operational automation. Phase 2 (6–12m): underwriting & compliance. Phase 3 (12–24m): full integration and optimisation. Staffing: 6–8 specialists (AI devs, data scientist, analysts, RPA/NLP experts). Budget: £540k–£700k over 2 years (+ cloud & licences). 5. Expected Benefits, Risks, Mitigations Benefits: higher accuracy, better recovery rates, enhanced compliance, personalised engagement, lower costs. Risks: Data privacy/security → mitigation: strong governance/encryption. Bias/accuracy → mitigation: regular audits. Integration issues → mitigation: phased agile approach. Regulatory risk → mitigation: close liaison with FCA/legal teams. 6. Summary & Conclusion AI adoption fits S&U’s cautious but forward-looking style. Clear business case: £540–700k investment for profitability
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