2015-01-21-奥纬咨询-Managing_Complexity_24页_325kb

Financial Services Complexity Analysis (Oliver Wyman, 2015)

Executive Summary Highlights

• Problem: Post-crisis, financial institutions increased scale and complexity during a bull run (1988–2006), but operational efficiency has not improved since 2001. Returns declined sharply (e.g., from ~20% to 7%), while costs rose similarly.
• Root Causes: Regulatory burdens, channel proliferation, fragmented IT, product explosion, geographic expansion, and technological pressures.
• Opportunity: Complexity management is not about elimination but optimizing costs (e.g., ~US$0.7–1.5Bn annually on compliance for large institutions in 2015–2017). Broader efficiency gains required radical transformation.

Key Findings

1. RoE Decline:

   • US/EEA banks saw RoE drop by 70% (GSIBs) or ~14% (non-GSIBs) since 2006, attributed to:
     • Capital/Liquidity Regulations: Post-crisis requirements suppressed margins.
     • Margin Compression: Ultra-low rates (e.g., US Rates peaked in 1988–2006).
     • Product-Focused Growth: Trade-offs in complexity vs. revenue, post-crisis adding layers of bureaucracy.

2. Drivers of Complexity:

   • Regulatory: Basel III/Solvency II, conduct rules (e.g., MiFID II); compliance costs up to 2–3% of total costs, slowing decision-making.
   • Channel/Geographical: Explosion of customer interaction channels and global expansion → increased operating expense but lower granularity.
   • IT/Systems: Siloed legacy systems → operational inefficiencies; M&A integration delays worsen fragmentation.

3. Consequences:

   • Operational Opacity: Managers struggle to assess performance, leading to irrational resource allocation.
   • Increased Risk: Regulatory audits, operational losses (e.g., ORX 2013 data), and misconduct scandals (e.g., Libor).

Solutions

• Manage Complexity, Don't Simplify Entirely:

  • Metric Alignment: Common metrics (avoiding “siloed” KPIs) to harmonize decision-making (e.g., automate RAROC/EVA usage).
  • Advanced Analytics: Leverage big data, AI for fraud detection, risk modeling, and real-time adjustments.
  • Process Standardization: Automate repetitive tasks → reduce labor costs (consistently cited example: bank shared services).
  • Empowerment: Delegate decisions to local expertise → improves agility but requires effective compensation tying to performance.
  • Culture: Embed transparency and accountability in hiring/promotion; reward middle-layer innovation.

• Long-Term Challenges:

  • Regulatory fatigue (new rules continuous in Basel IV/MiFID II) → requires funding and patience.
  • Cultural reform → case studies from oil/gas show that strong, human-first cultures drive efficiency via incentives and monitoring systems.

Implementation Roadmap

• Step 1: Audit complexity sources (consult report via survey rankings).
• Step 2: Prioritize simplification → divest underperforming business lines.
• Step 3: Execute tech/systems upgrades (3–5 years), with cultural change as an enabler.

Key Stakeholder Implications

• Investors: Shift capital to non-financials if institutions cannot demonstrate ~7%+ returns (utility levels).
• Managers: Must embrace command-and-control no longer feasible; decentralized, data-driven leadership is essential.
• Regulators: Mandates that constrain pay (e.g., bonuses vs. base salary) may unintentionally reduce agility and initiative.