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potential","31","939","\u002Fuk\u002Fen\u002Findustries\u002Fbanking-and-finance-unleash-your-transformation-potential-0",{"element":204,"typeTargetId":205,"typeEntity":206},"field-entity-reference","sector",{"element":207,"href":208,"type":209,"content":210},"a","\u002Fuk\u002Fen\u002Fadmin\u002Fstructure\u002Ftypes\u002Fmanage\u002Fsector","node_type","Sector","2025-05-06T13:24:01","insight","1650","\u002Fuk\u002Fen\u002Fputting-data-governance-heart-customer-redress-scheme",[216,222,285,288],{"element":217,"hideSeparator":218,"type":219,"wysiwyg":220},"pg-wysiwyg","0","wysiwyg",[221],"\u003Ch3>Soon, motor finance providers will learn of the Supreme Court’s ruling on the lawfulness of hidden commission in car finance agreements. \u003C\u002Fh3>\u003Cp>If the Court finds that lenders’ failure to fully inform customers about their car finance commission was unlawful, finance industry regulator \u003Ca href=\"https:\u002F\u002Fwww.fca.org.uk\u002Fnews\u002Fstatements\u002Fmotor-finance-review-next-steps#:~:text=Under%20a%20redress%20scheme%2C%20firms,to%20make%20sure%20they%20do.\">the Financial Conduct Authority (FCA) is likely to consult on a redress scheme\u003C\u002Fa>. This would require the finance sector to compensate hundreds of thousands of consumers. \u003C\u002Fp>\u003Cp>The FCA is already considering a redress scheme in response to complaints about car finance discretionary commission arrangements (DCAs), also relating to hidden commission. A redress scheme on this scale would have significant financial implications for the finance sector; it would also put providers’ data teams and systems under severe pressure. The FCA’s review last year \u003Ca href=\"https:\u002F\u002Fwww.fca.org.uk\u002Fnews\u002Fstatements\u002Fupdate-motor-finance-work\">of DCAs\u003C\u002Fa> – which saw it assessing thousands of records spanning 14 years – hints at some of the challenges for lenders. \u003C\u002Fp>\u003Cp>The FCA reported that, while firms engaged with them constructively, many struggled to supply the data needed within its requested timescale. Reasons for delays in providing data, it said, included, “firms not keeping older data, and data being stored on multiple systems, or being spread between lenders and brokers”. \u003C\u002Fp>\u003Cp>With this in mind, we are returning to data for the last in our series of blogs for lenders planning for potential remediation projects. Drawing on Talan Data’s experience in best practice remediation projects, this blog sets out why data governance matters.\u003C\u002Fp>",{"element":223,"imagePosition":224,"mediaImage":225,"title-255":236,"type":237,"expandsItems":238},"pg-expands","left",{"element":161,"mediaImage":226,"title":235,"type":137},{"element":128,"content":227},{"srcset":228,"sizes":229,"url":232,"alt":233,"width":170,"height":234},"https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner_400\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=ICTvtH0N 400w, https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner_700\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=BkvBsEFk 700w, https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=J41_ARts 1200w",{"400":230,"700":231,"1200":232},"https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner_400\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=ICTvtH0N","https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner_700\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=BkvBsEFk","https:\u002F\u002Fstatic.talan.com\u002Fs3fs-public\u002Fstyles\u002Fhalf_banner\u002Fpublic\u002F2025-06\u002FColleagues-reviewing-work-laptop.jpg.webp?itok=J41_ARts","Colleagues reviewing items on a laptop","806","Image of Colleagues reviewing items on a laptop","How a data governance framework underpins project success","expands",[239,245,249,253,257,261,265,269,273,277,281],{"element":240,"title-60":241,"type":242,"wysiwyg":243},"pg-expands-item","1. Shifting the focus to prevention rather than cure","expands_item",[244],"\u003Cp>Many remediation projects require historical scenarios to be reconstructed, which becomes extremely difficult with incomplete or inaccurate historical data. \u003C\u002Fp>\u003Cp>Strong data governance reduces the likelihood of issues requiring remediation in the first place, by ensuring data quality, integrity, and compliance with regulations.\u003C\u002Fp>",{"element":240,"title-60":246,"type":242,"wysiwyg":247},"2. Identifying the issues",[248],"\u003Cp>For example, incomplete or outdated customer information makes it challenging to identify and contact all affected customers, particularly those who have moved or changed contact details. \u003C\u002Fp>\u003Cp>Systematic data monitoring and controls help identify potential issues early, allowing for more proactive remediation before problems escalate.\u003C\u002Fp>",{"element":240,"title-60":250,"type":242,"wysiwyg":251},"3. Defining the scope of remediation",[252],"\u003Cp>Poor data quality makes it difficult to accurately determine how many customers are affected and to what extent, potentially leading to under or over-estimation of the scope of remediation. \u003C\u002Fp>\u003Cp>Well-documented data lineage and metadata help accurately define the scope of affected customers and products.\u003C\u002Fp>",{"element":240,"title-60":254,"type":242,"wysiwyg":255},"4. Enabling analysis of the root cause of issues",[256],"\u003Cp>Data gaps or inconsistencies can obscure the true root causes of issues, making it difficult to develop effective remediation strategies.\u003C\u002Fp>\u003Cp>Data governance frameworks provide visibility into data flows and dependencies, making it easier to trace issues to their source.\u003C\u002Fp>",{"element":240,"title-60":258,"type":242,"wysiwyg":259},"5. Facilitating remediation planning",[260],"\u003Cp>Without clear governance establishing defined roles and responsibilities, effort may be duplicated and resources focused ineffectively.\u003C\u002Fp>\u003Cp>Clear data ownership and stewardship structures establish accountability for remediation actions and decisions.\u003C\u002Fp>",{"element":240,"title-60":262,"type":242,"wysiwyg":263},"6. Assuring data accuracy",[264],"\u003Cp>Inaccurate data can lead to incorrect compensation calculations, resulting in further customer detriment or unnecessary costs to the business. \u003C\u002Fp>\u003Cp>Established data quality rules and validation processes ensure remediation calculations and customer communications are accurate.\u003C\u002Fp>",{"element":240,"title-60":266,"type":242,"wysiwyg":267},"7. Providing regulatory evidence",[268],"\u003Cp>Without reliable data, it becomes challenging to demonstrate to regulators that remediation has been complete and effective. This may result in confidence in regulatory reporting being undermined, potentially leading to additional scrutiny or penalties. \u003C\u002Fp>\u003Cp>Governance documentation provides essential evidence for regulators that remediation efforts are comprehensive and systematic.\u003C\u002Fp>",{"element":240,"title-60":270,"type":242,"wysiwyg":271},"9. Managing change",[272],"\u003Cp>Remediation projects can be large in scale, with cases ranging from the simple to complex exceptions, and data may need to be modified over the project duration. \u003C\u002Fp>\u003Cp>Governance change control processes help manage remediation-related data modifications while preventing unintended consequences.\u003C\u002Fp>",{"element":240,"title-60":274,"type":242,"wysiwyg":275},"10. Enabling effective monitoring and reporting",[276],"\u003Cp>Poor data quality makes it difficult to accurately measure the effectiveness of remediation efforts and identify any customers who may have been missed. \u003C\u002Fp>\u003Cp>Established data metrics and dashboards enable effective tracking of remediation progress and outcomes.\u003C\u002Fp>",{"element":240,"title-60":278,"type":242,"wysiwyg":279},"11. Driving effective and sustainable solutions",[280],"\u003Cp>When data is of poor quality significant resources must be diverted to cleansing and validation, rather than remediation activities. Additional validation and cleansing activities can typically extend project timelines. \u003C\u002Fp>\u003Cp>Governance frameworks ensure that resources are used effectively and that remediation solutions are durable by embedding controls that prevent recurrence of similar issues.\u003C\u002Fp>",{"element":240,"title-60":282,"type":242,"wysiwyg":283},"12. Retaining knowledge for the future",[284],"\u003Cp>Once a remediation project ends, its learning and experience can easily be lost over time. \u003C\u002Fp>\u003Cp>Documentation requirements preserve institutional knowledge about the remediation, which is valuable for future reference and regulatory inquiries.\u003C\u002Fp>",{"element":217,"hideSeparator":218,"type":219,"wysiwyg":286},[287],"\u003Cp>A key decision for lenders is the level of support needed in developing a remediation project and the extent to which that should be delivered by specialist partners. Potential risks, such as non-compliance, regulatory fines, data security breaches and inaccurate decisions and payouts, can be successfully mitigated by working with expert specialists. \u003C\u002Fp>\u003Cp>Talan Data and AI’s work is informed by two decades of experience in managing and supporting large-scale remediation programmes, including complex redress and data quality initiatives for the financial services sector. We have a 200-strong team of data specialists ready to prepare and support responses to an anticipated redress scheme – taking lenders from strategy through to implementation. \u003C\u002Fp>\u003Cp>In this blog series, we have outlined the essential characteristics of a redress scheme, including the importance of data accuracy and the effective deployment of artificial intelligence. You can read more here:\u003C\u002Fp>\u003Col>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.talan.com\u002Fuk\u002Fen\u002Fseven-steps-best-practice-motor-finance-redress-scheme\">Seven steps to a best practice motor finance redress scheme \u003C\u002Fa>\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.talan.com\u002Fuk\u002Fen\u002Fensuring-data-accuracy-redress-scheme-key-success-and-compliance\">Ensuring Data Accuracy in a Redress Scheme: Key to Success and Compliance\u003C\u002Fa>\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.talan.com\u002Fuk\u002Fen\u002Fcreating-redress-scheme-ai-powered-environment\">Creating a redress scheme in an AI-powered environment\u003C\u002Fa> \u003C\u002Fli>\u003C\u002Fol>",{"element":289,"embedExternalForm":290,"expert":291,"title-255":297,"type":298},"pg-form","\u003Ciframe src=\"https:\u002F\u002Fgo.talan.com\u002Fl\u002F688263\u002F2025-06-17\u002F2ds988\" width=\"100%\" height=\"700\" type=\"text\u002Fhtml\" 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