FIN-RESOLVE™ Compliance Intelligence & Transformation Framework

Financial Intelligence Network for Risk Evaluation, Surveillance, Optimization, and Learning-based Value Enhancement.

Created by: Oluwabukola Rachael Tiamiyu, Financial Compliance & Data Analytics Expert

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  • Fragmented data across banking and fintech platforms
  • Excessive false positives from outdated rule-based detection systems
  • Escalating compliance costs without measurable returns
  • Inconsistent audit readiness and regulatory breaches
  • Inability to predict and prevent emerging financial crime typologies

According to the Association of Certified Anti-Money Laundering Specialists (ACAMS, 2024), financial institutions lose over $180 billion yearly due to compliance inefficiencies and regulatory penalties. Without a unified intelligence model, organizations remain trapped in reactive, high-cost compliance cycles.

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Introduction to the FIN-RESOLVE™ Framework

In today’s financial ecosystem, compliance challenges have evolved beyond documentation—they demand intelligence, agility, and predictive insight.

Despite billions spent annually on anti-money laundering (AML) and fraud prevention systems, organizations continue to face:

FIN-RESOLVE™ transforms compliance from a cost center into a predictive value engine by:

  • Automating anomaly detection and investigation workflows
  • Integrating AI-driven risk scoring and behavioral analytics
  • Aligning institutional operations with dynamic regulatory frameworks
  • Delivering audit-ready insights and measurable compliance ROI
  • Building a continuous learning ecosystem that evolves with financial threats

Introducing the FIN-RESOLVE™ Framework

Drawing from her cross-industry experience in banking operations, AML investigations, and machine-learning-based financial modeling, Oluwabukola Rachael Tiamiyu developed FIN-RESOLVE™, a proprietary AI-driven compliance intelligence framework designed to unify data, risk, and regulation under one adaptive ecosystem.

What is FIN-RESOLVE™?

FIN-RESOLVE™ (Financial Intelligence Network for Risk Evaluation, Surveillance, Optimization, and Learning-based Value Enhancement) is a five-pillar compliance intelligence system that helps financial institutions anticipate, evaluate, and eliminate risk before it escalates into regulatory or financial loss.

Built on adaptive algorithms and real-time data fusion, the framework serves as both a strategic decision-support system and a diagnostic maturity model, empowering banks, fintechs, and regulators to achieve compliance excellence.

The Five Strategic Pillars of FIN-RESOLVE™

FUSE – Data Fusion & Anomaly Detection

Integrates structured and unstructured data from banking systems (Oracle FLEXCUBE, Dynamics 365, ERP modules) into one harmonized view.
Uses Python and SQL pipelines to cleanse, transform, and enrich data while identifying suspicious behavioral patterns.

Impact: Reduces manual investigation time by up to 45% and enhances data transparency across audit channels.

NORM – Dynamic Regulatory Alignment Core

Continuously synchronizes compliance models with global regulations (FATF, FinCEN, OFAC).
Uses ontology mapping and automated rule engines to ensure policy consistency and immediate response to regulatory changes.

Impact: Minimizes regulatory breaches and strengthens institutional credibility.

INSIGHT – Behavioral Intelligence & Graph Analytics

Employs machine learning and graph neural networks to map hidden relationships between entities, accounts, and transactions.
Detects collusive behaviors, money-laundering chains, and synthetic identities.

Impact: Decreases false positives by 30% and uncovers previously undetected fraud typologies.

OPTIMIZE – Predictive Risk Scoring & Financial Resilience Modeling

Uses Monte Carlo simulations (@RISK) and scenario forecasting (Quantrix Modeler) to project the probability and financial impact of compliance failures.
Provides actionable insights into cost optimization and performance improvement.

Impact: Improves fraud prevention accuracy by 35% and reduces compliance costs by 20%.

EVOLVE – Continuous Learning & Value Enhancement Loop

Implements adaptive feedback loops that refine detection algorithms based on new threat data and audit results.
Links compliance KPIs with strategic business metrics to demonstrate tangible ROI.


Impact: Builds a self-learning compliance culture, sustaining long-term operational integrity.

Use Cases and Impact Scenarios

Banking & Fintech Institutions

  • Achieve 40% faster AML investigations through automated risk scoring.

  • Strengthen audit preparedness and regulatory trust.

  • Reduce fraud losses while improving operational efficiency.

Regulatory & Oversight Agencies

  •  Gain unified dashboards for inter-institutional data collaboration.

  • Predict systemic risks before they cascade across financial networks.

Regulatory & Oversight Agencies

  • Integrate FIN-RESOLVE™ as a client diagnostic model for compliance maturity audits.

  • Deliver measurable transformation strategies for financial resilience.

Academic & Training Institutions

  • Adopt FIN-RESOLVE™ as a teaching model for applied AI in financial regulation.

  • Use its analytics modules to train compliance professionals on risk modeling.

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Let’s Build Smarter Compliance Together.

Integrity. Intelligence. Impact.

Transforming compliance into insight, and data into trust.