IDC PlanScape: Augmenting Compliance in Financial Services through Artificial Intelligence
This IDC PlanScape provides business and technology leaders in the financial services industry with a strategy for ensuring compliance through the implementation of artificial intelligence (AI) solutions. The study provides a high-level definition of AI, presents compelling reasons for AI implementations, outlines AI deployment phases, identifies key stakeholders, and highlights challenges financial organizations implementing AI must overcome."Financial services organizations have a fertile opportunity to ensure compliance via AI implementations. The need to analyze Big Data and to replace or complement labor-intensive and rules-based legacy systems presents an excellent case for machine learning and natural language programming algorithms to help curb financial crime." — Associate Research Director George Briford, IDC Financial Insights
Please Note: Extended description available upon request.
- IDC PlanScape Figure
- Executive Summary
- Why is AI in Financial Services Compliance Important?
- Minimization of the Risk of Business Restrictions and High Fines
- Improvement of Process Efficiencies
- Potential to Replace Legacy Technology with Models and Algorithms
- Provision of Agility to Compliance Supervisors
- What is AI in Financial Services Compliance?
- Who are the Key Stakeholders?
- How Can My Organization Take Advantage of AI TO ENSURE Financial Services Compliance?
- Implementation Challenges
- Technological Challenges
- Data
- AI Architecture: Cloud Adoption versus Internal Server Deployment
- Organizational and Process Challenges
- Demanding Steps: From Model Learning and Engineering to Validation
- Partner Selection
- Skills and Capabilities ("Buy versus Build")
- Regulatory and Legal Challenges
- The Key Phases of AI in Compliance Implementation
- 1) Build the Fundamentals
- Develop Basic Skills
- Outcome
- Formulate Business Priorities
- Outcome
- 2) Play and Learn
- Select the Variables: Algorithm Learning and Data Modeling
- Outcome
- Test the Explainability
- Outcome
- 3) Apply and Improve
- Model Validation
- Outcome
- Test the Scalability of the Overall Solution
- Outcome
- Advice for Technology Buyers
- Related Research
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