Driving growth in financial services with GenAI and third-party event data

Financial advisors, insurance agents and relationship managers can develop personalized and actionable recommendations.

In brief

• Financial institutions are using GenAI to extract actionable insights from vast data sets, improving decision-making and client engagement.
• External data sources offer a richer understanding of client behaviors and preferences, helping more personalized advice.
• Timely access to life event data, such as marriage or retirement, allows firms to proactively support clients during key transitions.

It’s no secret that the world of third-party data vendors is expanding, offering financial institutions access to a vast and ever-growing array of providers. These vendors track critical life events — marriages, births, real estate transactions and significant career changes — while also feeding behavioral and sentiment data directly into customer relationship management (CRM) systems. However, this landscape is complex, opaque and filled with overlapping capabilities, making it increasingly difficult for organizations to discern which providers offer truly unique value and which simply repackage similar data sets.

Navigating this fragmented and evolving ecosystem requires a thoughtful and strategic approach. Instead of piling on endless data streams, organizations must cut through the noise to identify vendors that align with their innovative goals, client needs and technical capabilities. The challenge isn’t just in selecting vendors — it’s in understanding where data sources intersect, where gaps remain and how to integrate disparate data sources effectively.

2. Data accuracy and fill rate reliability 

Accuracy and fill rate issues can significantly undermine the effectiveness of third-party data across the financial services industry. Inaccurate or incomplete data can lead to misguided strategies and lost opportunities. The challenge for firms in these sectors is to confirm that the data they rely on is both accurate and comprehensive. This involves:

3. Aligning use cases to vendor data 

Not all vendor data is created equal. Some vendors focus on demographic and firmographic updates, while others excel in detecting personal life events or providing detailed financial product usage patterns. The challenge for wealth and asset management firms is to map specific needs, like improving CRM accuracy or identifying timely rollover opportunities, to the right data streams.

This alignment process often involves:

4. Handling “wide” vendor data

If you’re working with a diverse portfolio of vendors, you might end up with very broad data sets, meaning huge volumes and diverse data points from multiple sources, which can be overwhelming to understand and integrate effectively. Dimensionality reduction techniques, such as principal component analysis (PCA), uniform manifold approximation and projection (UMAP) or t-distributed stochastic neighbor embedding (t-SNE) can help reduce complexity, but the process isn’t always straightforward and will almost always reduce interpretability of your data features. It requires data scientists and analysts who can work together to cut through the noise, focusing on the data points that truly drive client engagement and decision-making. It is critical that you have both data scientists who deeply understand the machine learning algorithms being used to transform your data and data analysts who can directly align business requirements with the transformations being performed.

Managing these wide data sets also calls for robust data governance frameworks. It’s not just about having data — it’s about having clean, actionable data that is comprehensively inventoried and tracked. The complexity of your data will scale directly with the complexity of the required documentation. 

5. Data integration 

Integrating external data into your firm’s systems is easier said than done. Different vendors use different formats and standards, leading to integration hurdles and potential quality issues. Without proper data harmonization efforts, you risk losing valuable insights in a sea of inconsistent or duplicate records.

Successful integration often involves:

• Building a flexible data infrastructure capable of scaling with new vendor feeds.
• Employing data transformation tools and AI-driven solutions to improve consistency.
• Establishing clear protocols for data quality checks and error resolution before pushing information into downstream systems (like your CRM or analytics platforms).

The value of third-party data hinges on its usefulness and reliability. Integration challenges can be overcome with the right people, processes and technology in place.

6. Expense justification

Demonstrating ROI for vendor data can be challenging, especially when data sets are costly and only a small percentage of features is relevant to your use case. The difficulty in directly linking vendor data to improved KPIs, such as client retention or targeting effectiveness, can further complicate justifying the investment.

To address this:

• Measure ROI by tracking performance metrics directly attributable to the vendor data, such as increased revenue or reduced churn.
• Conduct a cost-benefit analysis to compare the data’s costs with its potential business impact.
• Start with a phased rollout to validate value through small-scale use cases before scaling up.
• Leverage pilot results to negotiate better pricing or terms with the vendor.

This approach helps a clear understanding of the data’s value while managing costs effectively.

How third-party data can improve use cases within the financial services sector

1. Wealth and asset management: anticipating client needs and providing hyper-personalized advice

Wealth and asset management is built on trust and proactive engagement. By enriching first-party data with third-party insights, advisors can anticipate client needs before they arise, tailor their recommendations and create highly personalized financial strategies:

2. Insurance: smarter risk management and policy personalization

The insurance industry thrives on data-driven risk assessment. Third-party data supports insurers to better predict risk, improve policies and proactively support clients during critical life moments:

3. Banking: driving smarter lending, improving customer engagement and personalizing banking solutions

Consumer and commercial banks rely on first-party data, such as transaction histories, credit scores and account balances. However, third-party data can unlock deeper insights into customer behavior, financial health and risk profiles. This proactive approach allows banks to tailor their offerings, improve customer engagement and foster long-term relationships. As customers navigate various life transitions, leveraging third-party data not only facilitates personalized service but also empowers banks to deliver timely and relevant solutions that align with their customers’ evolving financial landscapes:

Katherine Rusk- Senior Manager and Christopher Tetro - Manager at Ernst & Young LLP also contributed to this article.