Conversations between bank employees and customers about complex financial products are subject to strict legal and regulatory standards. This stronger oversight, especially in the U.S. and EU, emerged in response to longstanding concerns that some banks were overselling or mis-selling complex products to customers. In turn, many banks adopted policies to curb misconduct risk, including enhanced training, closer managerial review of client interactions, and the use of sales-pattern monitoring to flag potential problems.
However, putting these policies into practice requires banks to sift through massive volumes of customer interactions, complaints, and transaction data. Much of this information is unstructured—emails, call notes, complaint narratives—and difficult to analyze at scale. As a result, even well-designed conduct-risk policies are often undermined by practical data-analysis challenges: noisy alerts, fragmented information, and limited ability to connect customer concerns to employee behavior in a timely way.
Analyzing customer complaints
Customer complaints are one of the most valuable early indicators of potential misconduct, but they are also among the hardest to analyze effectively. Traditional monitoring approaches produce large volumes of alerts, many of which are false positives, overwhelming compliance teams and diluting attention from real risks.
Banks have long struggled to connect unstructured complaint narratives with employee actions and sales transactions. Without this linkage, it is difficult to identify patterns of mis-selling or other conduct concerns until they become systemic problems. As an EY assessment pointed out, “…compliance efforts are not sufficiently aligned to risky patterns, behavior, performance and controls.”
To address these limitations, Teradata developed the Customer Complaint Analyzer, which applies advanced language-modeling techniques to extract meaning from unstructured complaint data. The solution:
- Identifies complaint topics through topic modeling
- Assesses dissatisfaction levels with sentiment analysis
These insights are appended directly to customer records, giving investigators a richer context and enabling more targeted, data-driven reviews of potential misconduct.
Monitoring banker outbound emails
Employee-to-client email is another vital data source for identifying misconduct risks such as insider trading, unauthorized disclosures, unsuitable recommendations, or conflicts of interest. Yet traditional monitoring often relies on lexicon rules or manual sampling—approaches that generate high alert volumes without capturing the nuance of real communication patterns.
To advance beyond these limitations, one global systemically important bank (G-SIB), partnered with Teradata to modernize surveillance of outbound employee emails. Leveraging open-source language models within Teradata ClearScape Analytics®, the bank implemented real-time monitoring and automated alerting capable of detecting subtle signals of potential misconduct.
The impact was significant: risk exposure decreased, regulatory compliance strengthened, and operational efficiency improved as manual review volumes fell dramatically. Investigative teams could focus on higher-value cases rather than screening large numbers of low-quality alerts.
For this G-SIB, the principle behind the effort is simple: “Trust, but verify.”
Unlock smarter conduct analytics with Teradata
The volume and complexity of customer interactions are growing faster than traditional compliance and monitoring approaches can handle. To keep pace, banks need analytics solutions that can turn unstructured data into actionable insights.
Contact Teradata today to learn how our advanced analytics and generative AI solutions can help your bank transform your data into smarter, faster, and more effective conduct-risk management.