Objectives:

Upon completion of this course, participants will be able to:

• Analyze the operational and regulatory benefits of implementing **Automated Complaint Analysis (ACA)** systems.
• Apply **Natural Language Processing (NLP)** techniques (e.g., topic modeling, sentiment analysis) to unstructured consumer complaint data.
• Design a robust system for **automatic categorization and routing** of complaints to the correct department and severity level.
• Utilize ML models to proactively identify **emerging complaint trends** and flag systemic product or process flaws (Root Cause Analysis).
• Develop a dashboard of **Key Risk Indicators (KRIs)** based on complaint data for reporting to senior management and regulators.
• Ensure the ethical use of customer data in ACA, managing privacy and preventing algorithmic bias in categorization.
• Evaluate the impact of ACA on complaint resolution times, regulatory reporting, and internal audit effectiveness.
• Integrate the ACA system with the broader Compliance Management System (CMS) and Quality Assurance (QA) functions.

• Customer Service and Complaint Handling Managers
• Data Scientists and Analysts focused on NLP and Text Analytics
• Compliance Officers and Market Conduct Specialists
• Internal Auditors and Operational Risk Managers
• Product Development and Legal Counsel Teams
• Regulators overseeing Consumer Recourse and Market Conduct
• Heads of Quality Assurance and Process Improvement

• Case Studies analyzing how ACA systems detected and remediated major systemic product flaws.
• Group Activities on designing a complaint categorization hierarchy for a banking product line.
• Discussions on the ethical use of sentiment analysis and the risk of algorithmic bias in prioritization.
• Individual Exercises on interpreting a sample output from an NLP topic modeling system.
• Workshop on developing a structured process for translating ACA insights into policy/product changes.
• Demonstration of popular NLP and text analytics toolkits for financial data.

Unit 1: The Strategic Value of Complaint Analysis

• Defining the regulatory and business imperative for efficient complaint handling.
• The shift from manual tracking to **Automated Complaint Analysis (ACA)**.
• ACA as a critical early warning system for UDAAP and systemic risk.
• The economic case: Reducing resolution time and operational costs.

• Identifying relevant data streams: Emails, call transcripts (Speech-to-Text), social media, external reviews.
• Challenges of unstructured data: Noise, ambiguity, misspellings, and emotional language.
• Techniques for data cleaning, anonymization, and feature engineering for NLP.
• Compliance with data privacy rules in processing sensitive complaint details.

Unit 2: NLP and Machine Learning for Analysis

• Applying **Natural Language Processing (NLP)** techniques (tokenization, vectorization) to complaint text.
• Implementing supervised ML models for automatic **topic classification** and routing (e.g., loan fees, late payment, fraud).
• Using **Sentiment Analysis** to gauge customer frustration and risk severity.
• Designing the decision logic for automated escalation and prioritization of high-risk complaints.

• Utilizing **topic modeling** (e.g., LDA) to discover latent, emerging complaint themes.
• Linking complaint categories to specific product features, processes, or employee training gaps.
• Automating the detection of **systemic failure indicators** and outlier complaints.
• Generating actionable insights for product redesign and policy changes.

Unit 3: Implementation, Governance, and Reporting

• Integrating the ACA system with CRM, internal ticketing systems, and regulatory reporting platforms.
• Developing a robust **validation process** for the accuracy and fairness of the ML models.
• Implementing a **human-in-the-loop** mechanism for quality assurance and continuous model training.
• Defining internal policy for handling automatically prioritized, high-risk complaints.

• Developing a dashboard of **Key Risk Indicators (KRIs)** for C-suite and Board reporting.
• Tracking time-to-resolution, complaint volume by product, and root cause frequency.
• Automating regulatory reporting requirements for complaint data submission.
• Using ACA to support internal audit and compliance testing of market conduct.