The Financial Conduct Authority (FCA) has set out regulations designed to protect the interests of financial services consumers. In simple terms, this means that financial services businesses must provide value for money and make it easier for their customers to buy the right products – and complain or cancel those products if they’re unhappy. An effective data strategy can support financial services in meeting consumer duty regulations and staying ahead in an ever-changing regulatory environment.
Every one of us uses financial services to some degree, for banking, loans, insurance, or investments. The myriad of product offerings can seem vast and at times overwhelming to those not in the business. There is little trust in the industry with Money Week reporting “fewer than half of UK adults, equivalent to 21.9 million people, had confidence in the UK financial services industry and just 36% agreed that most financial firms are honest and transparent.”
The introduction of consumer duty regulation attempts to redress that statistic. Compliance with consumer duty regulations is not only a legal necessity but also a crucial component in building trust with clients. As regulatory frameworks evolve, financial institutions must adopt robust data strategies to ensure compliance while also enhancing operational efficiency.
What is Consumer Duty Regulation?
Consumer duty regulations are designed to protect the interests of financial services consumers by ensuring transparency, fairness, and accountability. They require financial institutions to act in the best interests of their clients, provide clear and comprehensible information, and avoid conflicts of interest. Meeting these requirements demands a meticulous approach to customer data management.
Compliance is not just about ticking boxes; it’s about building and maintaining trust. An effective data strategy ensures that clients’ interests are prioritised, fostering a stronger and more trusting relationship between financial institutions and their clients.
Under the duty, financial services consumers should expect communications they understand, products and services that meet their needs and offer fair value. The FCA has said it wants to see evidence recorded of good outcomes for financial services customers when it comes to products and services, price and value, consumer understanding and consumer support.
Data strategy for consumer duty compliance:
A good data strategy sets out plans, policies, and infrastructure to unlock the value of data within the business and is transformative for the growth and success of the company. Delivery of consumer duty will rely on every financial services provider knowing the customer well and recording interactions with the customer.
- Data Governance: Implementing a robust data governance framework is the foundation and groundwork required to deliver a successful data strategy. This involves defining roles and responsibilities, establishing data quality standards, and ensuring compliance with data protection regulations. A well-structured governance model helps financial institutions maintain accurate and reliable data, a cornerstone of consumer duty compliance. This involves a strategic approach to data capture, management, and analysis to provide concrete evidence supporting their compliance with consumer duty regulations. In practice, this is useful for managing things like tracking data sources to evidence compliance during audits.Good data governance will support the establishment of strong relationships between financial institutions and their suppliers. For instance, in the insurance sector, collaborating with third-party claims assessors is essential. These assessors provide granular data, ensuring that insurers meet the stringent requirements of consumer duty regulations.
- Customer Relationship Management (CRM) Systems: CRM systems play a crucial role in managing client interactions and ensuring that financial services are aligned with client interests. Integrating CRM systems into the data strategy allows for a comprehensive view of client relationships, aiding in the identification and mitigation of potential conflicts of interest. A key example is the management of “Open and Closed Accounts”. Applying the principles of consumer duty, compliance extends to the meticulous management of customer accounts, particularly in scenarios like “Open and Closed Accounts.” This involves applying the correct products and available interest rates, adhering to the “know your customer” principle, and addressing dormant accounts appropriately.
- Data Analytics for Compliance Monitoring: Leveraging data analytics tools enables financial institutions to monitor and analyse vast amounts of data to ensure compliance with consumer duty regulations. Predictive analytics can identify potential areas of non-compliance, allowing proactive measures to be taken. Moreover, data analytics can help in assessing the impact of regulatory changes and adapting compliance strategies accordingly.
Under consumer duty regulations, customers should receive clear communications, products, and services that meet their needs, and timely customer support.
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- Clear Communications (Product Design – Reference Data)
In the realm of product design, clear communication is vital. Financial institutions must leverage reference data to ensure that their product designs are easily understandable by consumers. - Products and Services that Meet Needs (Compliance & Risk)
Compliance and risk management play a pivotal role in offering products and services that meet customer needs. Robust data capture, master data management, and correct reference data are critical elements in providing fair value. - Timely Customer Support (Complaints & Claims Procedures)
To fulfil the duty of offering timely customer support, financial institutions must implement effective complaints and claims procedures. Data capture, management, and analysis are essential in addressing customer needs promptly.
- Clear Communications (Product Design – Reference Data)
4. Automation and AI: Embracing automation and artificial intelligence can streamline compliance processes. Machine learning algorithms can analyse vast datasets to identify patterns and anomalies, assisting in the early detection of potential compliance issues. Automation also reduces the risk of human error in compliance-related tasks.
The regulatory landscape is constantly evolving. An agile data strategy enables financial institutions to adapt quickly to changes in consumer duty regulations, reducing the risk of non-compliance and potential legal consequences. Having the data governance groundwork in place can deliver the agility required.
Conclusion:
In the ever-evolving world of financial services, a strategic and proactive approach to data management, coupled with strong business practices, is indispensable for compliance with consumer duty regulations. By investing in a robust data strategy that encompasses governance, data quality, analytics, and automation, financial institutions can not only meet regulatory requirements but also enhance operational efficiency and build enduring trust with their clients. As consumer duty regulations continue to shape the industry, a well-executed data strategy will be a key differentiator for those aiming to thrive in the competitive financial services landscape.
About the Author
Stephen Pass, Sector Lead – Insurance & Professional Services
Stephen is a Sector Lead for Insurance and Professional Services at Sempre Analytics. Stephen brings with him a strong data and analytic background, having held positions in leading global companies such as Teradata and Lloyds Banking Group, this awarded him valuable international experience in enterprise level software and services sales strategy.
Stephen’s deep experience solving business problems helped the Banking and Insurance industry make the best of opportunities from digitalisation. His strategic approach in identifying and understanding the market direction from the customer needs, alliances and the competitive landscape enabled his clients to develop their data analytics roadmap to become more operationally efficient and truly data-driven.
