Risk & Compliance

Industry Trends

The financial services industry is facing unprecedented challenges and opportunities in the areas of risk and compliance. The rapid pace of digital transformation, the increasing complexity of regulatory requirements, the growing threats of cyberattacks, and the heightened expectations of customers and stakeholders are all driving the need for more effective and efficient risk and compliance management. Financial institutions that can successfully navigate these challenges and leverage these opportunities will gain a competitive edge and enhance their reputation and performance.

The Challenge

Managing risk and compliance in the financial services industry is not a simple task. It requires a holistic and integrated approach that covers multiple dimensions of risk and compliance. The dimensions of risk and compliance are interrelated and interdependent, and they require a coordinated and consistent response from the financial institutions. However, many financial institutions face challenges in achieving this level of coordination and consistency, due to factors such as:

  • Siloed and fragmented risk and compliance functions and processes.
  • Lack of a clear and comprehensive risk and compliance strategy and governance.
  • Inadequate and outdated risk and compliance data and technology.
  • Insufficient and unskilled risk and compliance talent and culture.

The Opportunity

Despite the challenges, financial institutions also have a great opportunity to transform their risk and compliance functions and processes into a source of competitive advantage and value creation. By adopting a more proactive, agile, and innovative approach to risk and compliance management, financial institutions can:

  • Enhance their risk and compliance performance and resilience.
  • Reduce their risk and compliance costs and inefficiencies.
  • Improve their risk and compliance insights and decision making.
  • Increase their risk and compliance trust and transparency.
  • Support their business growth and innovation.

Leading Practices

To seize the opportunity and overcome the challenge, financial institutions need to adopt and implement leading practices in risk and compliance management. These practices include:

  • Aligning risk and compliance strategy and governance with the business strategy and objectives.
  • Integrating risk and compliance functions and processes across the organization and leveraging synergies and efficiencies.
  • Investing in risk and compliance data and technology to enable automation, analytics, and artificial intelligence.
  • Developing and retaining risk and compliance talent and fostering a risk and compliance culture.
  • Engaging and collaborating with regulators, customers, and other stakeholders to build trust and confidence.

Our Offerings

We offer a comprehensive range of AI & Advanced Analytics solutions and services, tailored to the specific needs and objectives of the financial services sector, such as:

  • BSA Compliance: ensuring compliance with the Bank Secrecy Act and other anti-money laundering and counter-terrorism financing regulations.
  • Operational Risk: identifying, assessing, monitoring, and mitigating the risks arising from internal processes, systems, people, and external events.
  • Capital Markets Compliance: complying with the rules and regulations governing securities trading, derivatives, and other financial instruments.
  • Cybersecurity: protecting the confidentiality, integrity, and availability of information and systems from cyber threats and incidents.
  • Regulatory Compliance & Remediation: responding to the demands and expectations of regulators and resolving any issues or deficiencies in a timely and effective manner.
Slide 1
Commercial Card Analytics

A national bank effectively addressed fraud risk and improved customer engagement in its commercial card business by implementing advanced data analytics and machine learning to analyze merchant card usage, resulting in enhanced fraud detection, increased customer loyalty, and boosted revenue.

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Predictive lending, Cross-Sell/Up-Sell:

A large national bank boosted its lending revenue by developing a data-driven solution that utilized machine learning to analyze historical and external data, identifying deposit clients with high borrowing propensity and optimizing communication channels for loan and mortgage offers, resulting in increased revenue, improved customer retention, and enhanced bank reputation.

Predictive lending, Attrition Forecasting
Predictive lending, Attrition Forecasting

A major bank improved borrower retention by developing predictive lending models that utilized machine learning to analyze personal, financial, and behavioral data, identifying borrowers likely to refinance with other lenders and optimizing retention strategies, resulting in reduced attrition, increased customer satisfaction, and enhanced market share.

Deposit Attrition, Business Banking:
Deposit Attrition, Business Banking:

A bank increased its deposit retention rate by developing a data-driven solution that utilized advanced analytics to segment customers, created personalized offers, and leveraged multiple communication channels, resulting in higher customer satisfaction and additional revenue.

Wealth Management – Attrition, Segmentation
Wealth Management, Attrition, & Segmentation:

A bank grew its wealth management business by implementing a data-driven solution that utilized analytics and machine learning to segment customers based on financial health and life events, providing personalized solutions and communication strategies, resulting in improved customer satisfaction, loyalty, and increased market share and revenue.

liquidity management
Liquidity Management

A bank enhanced its liquidity management by developing an advanced analytics and machine learning system to accurately predict daily cash flow and optimize liquidity management, resulting in improved cash flow forecast accuracy, increased return on excess cash, and enhanced operational efficiency and compliance.

Payout Process Transformation​
Payout Process Transformation​

A leading insurance provider improved its payout process by implementing a cloud-based platform that transformed 86% of checks to digital, reduced fraud and errors by 92%, and achieved an annualized ROI of over 500%, while enhancing customer satisfaction by 30% and completing the transformation in less than 6 weeks.

Enhancing Fraud Detection and Enforcement for a US Financial Regulatory Agency
Enhancing Fraud Detection and Enforcement for a US Financial Regulatory Agency

A US financial regulatory agency enhanced fraud detection and enforcement by implementing a new Enterprise Data Warehouse platform that leveraged advanced analytics and machine learning to process and analyze data from over 6 billion records, resulting in a 20% increase in enforcement actions, improved data accuracy, and enhanced agency reputation.

Case Study: Post M&A Operational Alignment​
Post M&A Operational Alignment​

A large FinTech company addressed post-M&A challenges by implementing enhanced SDLC methodologies, consolidating key business functions, and realigning staff, resulting in a 20% reduction in operational costs, a 75% reduction in case backlog, and a 98% customer renewal rate, all while achieving substantial improvements in operational efficiency and customer satisfaction.

Case Study: Strategic Roadmap for a Global Bank​
Strategic Roadmap for a Global Bank​

A global bank overcame challenges in a competitive financial sector by partnering to develop a strategic roadmap for data and analytics transformation, leading to improved customer satisfaction and retention, increased revenue and profitability, enhanced operational efficiency, and better decision-making through a 360-degree view of the customer journey and optimized processes.

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MCG's team possess deep industry expertise, enabling them to craft actionable strategies, implement them effectively, and achieve tangible results.