The financial services industry is flooded with data—every transaction, interaction, and regulatory filing adds to an ever-growing volume of information. While the industry is continuously evolving to leverage this data with new tools, a persistent challenge remains: connecting and understanding this data effectively. Fragmented customer records and duplicate data continue to multiply, outpacing resolution efforts and causing significant inefficiencies.
The financial impact of poor data quality is staggering. In a recent webinar, we shared how one enterprise insurance company estimated $24 million in costs tied to manually reviewing and resolving duplicate records. Traditional Master Data Management (MDM) systems often fall short, focusing only on well-formed records while neglecting vital customer interactions. This limited scope prevents organizations from fully understanding their customers, leading to missed opportunities for personalization, targeted marketing, and improved customer retention.
Despite expectations that MDM should provide a complete, accurate view of customers, Gartner states that 75% of MDM programs fail to deliver anticipated business value. The primary issue? A disconnect between business objectives and IT priorities. While businesses seek actionable insights, IT teams often prioritize technical functionality, leading to misalignment. Additionally, the long implementation timelines of legacy MDM systems make them susceptible to shifting business needs, reducing their effectiveness.
But first, what is advanced entity resolution? Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity (such as a person, company, or product) across multiple datasets, despite differences in how they are represented. It is used to remove duplicates, merge fragmented data, and create a unified view of entities. Unlike traditional matching techniques, which struggle with imprecision and limited data, advanced entity resolution leverages AI, machine learning, and referential matching to improve accuracy. These methods account for inconsistencies like name variations, typos, and missing details, significantly enhancing identity resolution.
There are three key areas where traditional MDM often falls short, and where advanced entity resolution provides a superior approach. The three main pitfalls of traditional MDM are:
- Precision: The match engine lacks precision due to not utilizing newer technologies and failing to incorporate additional data points. Advanced entity resolution overcomes this by reducing false matches, in which improves data quality.
- Narrow Focus: The narrow focus on improving the matching engine’s accuracy, without attention to automating stewardship or expanding the match range, leads to missed matches. Advanced entity resolution overcomes the narrow focus of traditional MDM by using AI-driven, probabilistic, and multi-source matching techniques to unify records across diverse and evolving data landscapes.
- Limited Data: The use of limited data within MDM, focused solely on “core” data, along with the lack of external data integration and unaddressed sparsely populated records, restricts the match perspective. Advanced entity resolution overcomes this by integrating data from multiple sources, matching to adapt to evolving data variations and uncover hidden relationships.
What exactly does advanced entity resolution deliver?
- Higher Matching Accuracy – AI-driven algorithms identify potential matches even when traditional methods fail.
- Reduced Manual Effort – Automated processes minimize the need for manual data stewardship.
- Integration of External Data – Third-party reference data, such as historical name and address changes, enhances match confidence.
- Better Use of Sparse Data – Tiered matching allows financial institutions to incorporate thin data sources like chat logs and marketing interactions without compromising data integrity.
Advanced entity resolution has numerous beneficial use cases and real business impact. The following table summarizes the illustrative business benefits of advanced entity resolution in financial services shared in the recent Verato & Q Spark Group webinar:
| Benefit Category | Specific Improvement | Metric |
| Marketing Effectiveness | Improved Campaign Response Rate | +12% |
| Marketing Efficiency | Reduced Campaign Spending | -8% |
| Customer Retention | Increased Policy Renewal Rate | +20% |
| Operational Efficiency | Reduced Manual Duplicate Resolution Costs | $24 Million |
| Data Quality | Reduction in False Negatives | Over 1 Million |
| Data Quality | Reduction in Potential False Positives | 40,000 |
| Data Integration | Increased Utilization of Previously Unusable Data | 50% Match Rate |
By adopting advanced entity resolution, financial institutions can enhance data quality, improve customer engagement, and drive significant cost savings—turning fragmented data into a strategic asset to achieve Customer 360.
