Data Management in the Car Finance Scandal

Before any compensation can be calculated, the relevant data must be found. This is often the first and most significant hurdle. Financial institutions deal with a patchwork of legacy systems, departmental silos, and varying data retention policies. The data related to historic car finance agreements could reside in core banking systems, dealer management systems, archived records, and CRM systems. 

Talan Data x AI offers expertise in data analytics, data architecture, and solutions integration to tackle this challenge. Our experience in large-scale remediation programs, including complex redress and data quality initiatives, means we can help across the full lifecycle of the process, from strategy through to implementation.

Once identified, the ingested data will inevitably be messy. Duplicates, inconsistencies, missing fields, and varied data formats are par for the course. Think of a customer's name being recorded as "John Smith" in one system and "J. Smith" in another, or addresses having different abbreviations. 

Talan emphasises the importance of robust data quality frameworks, which include data cleansing, validation, standardisation, and traceability mechanisms. Our team can implement solutions to help to mitigate issues like duplicate records and unstructured data, which are common in remediation projects involving legacy systems. Talan's data-driven approach, informed by two decades of experience, is designed to ensure data is accurate, complete, and timely.

Redress projects are subject to intense scrutiny from regulators, legal teams, and the public. Every calculation, every decision, and every communication must be transparent and auditable. 

Talan helps clients build data lineage frameworks that are critical for demonstrating how data flows through systems and how decisions are made, embedding traceability and auditability into data pipelines. This is especially important for financial firms that need to meet the Financial Conduct Authority (FCA)'s expectations around accountability and contestability.

The data involved in a redress project is highly sensitive, containing personal financial information, historical transactions, and potentially health-related data. A data breach at any stage of the project would be catastrophic, compounding the original harm. 

Talan can help businesses implement robust security measures such as encryption and access controls to secure data and prevent unauthorised access. We can enable solutions that help clients adhere to various data protection regulations (like GDPR) by structuring data and implementing policies and procedures to ensure data handling aligns with legal requirements, whether on-premise or on cloud-based platforms

Once the data is clean and secure, it needs to be leveraged to identify affected customers, quantify their detriment, and calculate appropriate compensation. 

Talan supports the use of advanced analytics and artificial intelligence (AI) to enhance the redress process. Our specialist knowledge helps lenders effectively apply AI technologies to remediation projects, which may include examples such as document analysis, data quality management, and process automation. Our work is informed by our experience in managing and supporting large-scale remediation programs, including PPI and Interest Rate Hedging Products.