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Strengthening financial relationships through advanced analytics

Great data drives excellent client experiences. This, in turn, leads to a more loyal and engaged client base. Relationship managers are crucial to maintaining client relationships but are limited by the available information. The ability to analyze large amounts of data to produce valuable insights is crucial for strengthening relationships and boosting revenue. However, achieving this task through human labor alone can be difficult.


Optimizing banking relationships with integrated data analysis

Our client, one of the world’s largest multinational financial institutions, wanted to support their relationship managers in optimizing their relationship-building efforts.

Dynamic data for deeper banking Insights

Relationship managers relied on static and transactional data and client experience to understand each client’s needs. This manual data extraction and ad-hoc processing was time-consuming, delimiting the scope and efficiency of their relationship-building efforts.

Comprehensive client profiles with banking data

Our client already had all the data needed to achieve their goals. However, they needed a way to consolidate and analyze it. Their goal was to create a comprehensive information source about each client. Equipped with this information, relationship managers would then be able to enhance client engagement through proactive management. They would also boost revenue through newly identified opportunities.


Mapping the future of banking with AI-driven insights

We held discovery workshops with the client’s relationship managers and business and analytics teams. We wanted to understand what the client needed from their data fully. The outcome was a consolidated understanding of the corporate client and relationship manager personas and the relationship managers’ information needs.

Our Customer Genomics platform generated insights for relationship managers. Previously untapped resources — such as peer recommendations, improved balance, fund-flow tracking, and news and regulatory alerts — now contributed to the broader picture of each client. Harnessing machine learning, deep neural networks, Bayesian probability, and network analysis made relevant information from five distinct workstreams easily accessible now:

  • Descriptive portfolio analytics

  • New prospecting opportunities

  • Leveraging of unstructured data

  • Behavior-based triggers

  • Next best action

What we provided:

Empowered relationship managers with AI analytics

Fractal designed an AI-powered solution providing a 360-degree view of the client. This formed the basis for AI-driven analytics to analyze client fundamentals, interactions, transactions, external events, credit, risk, and performance. After developing a centralized data hub, we created five use cases by applying AI techniques to the Client 360 data foundation.

We ranked the generated insights using the following cause-and-effect style parameters:

Parameter Description Target outcome

View peer comparison

Analyze client data against peers to identify areas of improvement

Deepen client relationships

Utilize LC confirmation and negotiation

Streamline letter of credit processes with AI negotiation tools

Deepen LC trade

Leverage the AI engine

Apply AI to monitor and engage with clients continuously

Track client engagement

Identify FX opportunities

Spot foreign exchange opportunities to prevent loss of revenue

Stop revenue leakage

Track significant fund flow

Monitor large fund movements for business opportunities

Improve average balances

Track new regulatory triggers

Keep up to date with regulatory changes affecting clients

Enhance client engagement and mitigate risk

Generate portfolio insights

Provide in-depth analysis of client portfolios for cross-selling opportunities

Improve product penetration

Review peer-based recommendation

Compare client data with successful peers for targeted recommendations

Activate new products for existing clients

Identify network opportunities

Deploy network analysis to find unbanked but related entities

Identify unbanked subsidiaries


Maximizing client engagement and revenue

The immediate impact:

Upon deployment, relationship managers were equipped with an AI-powered insights hub. It provided them with more comprehensive information on each customer, enabling them to optimize their relationship-building efforts, using:

  • Prioritized lists of prospective clients with relevant financial information

  • Recommendations of the next best product to improve up-sell and cross-sell opportunities

  • Firmographics to deepen existing client relationships and enhance long-term revenue potential

The long-term benefits:

Over time, the immediate benefits will translate into increased sales, efficiency, and better client experience. This improved client engagement is set to enhance revenue generation through:

Regular, actionable insights

Refined targeting and onboarding of new clients

More relevant, personalized communication

Access the PDF version of this case study for convenient reference and easy sharing.

Download PDF Putting the spark in your client relationships

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