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Investment Banking

Scout transforms customer onboarding for a global investment bank by reducing operational cost by 30%

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Investment Banking

Transforming Customer Onboarding for a Global Investment Bank with Scout by reducing operational cost by 30%

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The Challenge

A leading U.S.-based investment bank aimed to optimize its Global Markets Operations, with a focus on improving customer onboarding and trade settlement processes. The initiative sought to analyze the ‘cost to serve’ for top clients while enhancing the end-to-end customer experience.

Industry

Investment Banking

Location

US

Attempted Solution before Scout

Extensive efforts included conducting interviews, analyzing thousands of customer interactions, and creating

200

dashboards

The bank assembled a team of senior associates and consulting partners to develop a sustainable solution. Despite extensive efforts, including interviews, analysis of thousands of customer interactions, and the creation of 200 dashboards, the results were insufficient.
The solutions lacked deep insights into critical metrics and failed to provide a comprehensive view of the end-to-end process, leaving essential questions unanswered.

Enter

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The Head of the Global Market Division introduced Scout to provide data-based, near real-time insights into the core business questions. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems.

The AI analyzed the ‘cost to serve’ by breaking down total team effort across applications, emails, and documents for each client.

It was able to measure this across three metrics:

Scout's AI Model

could be quickly put into action because it didn't need complicated integration with current systems.

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Responsiveness

The speed at which the client receives a response.

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Seamless experience

Assessed by the number of interactions needed per case.

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Variations

Identified client-specific variations or ways in which clients were serviced.

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Responsiveness

the speed at which the
client receives a response.

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Seamless experience

assessed by the number of
interactions needed per case.

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Variations

identified client-specific
variations or ways in which
clients were serviced.

Scout’s AI to “find and fix”

Step 1: Find

As the first step, the AI decoded the work patterns of the customer onboarding team and connected them to business activities, by analyzing interactions between the onboarding team and their systems. It then automatically classified these work patterns as either core or non-core activities.

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Within two weeks, based on this analysis, Scout’s AI model delivered the following insights:
  • Identified specific pain points, such as the team's heavy reliance on MS Outlook and Excel.
  • Uncovered an opportunity for email and process automation, which could boost efficiency by nearly 10% through toil reduction.
  • Suggested automating data flow between external websites, spreadsheets, and Outlook, which could further enhance efficiency by 17%.

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Step 2: Fix

Armed with these insights, the management team initiated a series of quick and deep fixes to address the identified issues.

Quick Fixes

These were ‘no-code’ fixes based on standardisation and user training.
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Standardization of Data Inputs

The management team enforced standardization of data inputs across teams in the Customer Onboarding, Equity Essential Services/Trade (Buy & Sell), Security Settlement, and Prime Brokerage functions. This standardization enabled the generation of new metrics and insights.
This fix -

  • Reduced the total effort required to service clients
    by the Global Market Operations teams and lowered
    cost to serve per client.
  • Allowed teams to focus on high-impact
    activities, better align workforces, and deliver an improved,
    seamless client experience.
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Standardization of Reporting

Specific reports were developed using Scout data to provide a unified view of effort and touchpoints for each client service request. These reports tracked:

  • Effort spent per client at various lifecycle stages.
  • The number of follow-ups per client.
  • The total effort expended across the entire service process.

This standardization allowed for a clearer understanding of resource allocation and client interactions at every stage.

Deep Fixes

Systemic and long-term fixes planned across the organisation.
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Scout also provided detailed insights into the cost of unintegrated underwriting systems and applications, as well as the disconnection debt within the organisation.

Armed with these insights, the management team initiated a comprehensive transformation program to eliminate, automate, and transition low-impact workloads that delivered minimal value to both clients and the bank. This enabled more effective workload prioritization.

Additionally, the customer journey view in Salesforce CRM was enhanced by integrating Scout platform data. This integration provided Client Relationship Managers, Functional Leads, and Leadership with a single source of truth, offering visibility into key metrics and enabling them to monitor the impact of actions and interventions seamlessly.

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It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e.

  • Getting teams to work together across functions
  • Fixing disconnected systems
  • Simplifying and optimizing work processes
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Business outcomes

In summary, Scout’s intervention led to significant improvements:

40%

Improvement in Turnaround Times:

For customer service and requests

30%

Reduction in Operational Costs: Streamlining

processes and eliminating inefficiencies

15%

Enhanced Revenue from Operations: Attributed

to increased trade volumes from large customers

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Elimination of Low-Impact Workloads:

Enabling prioritization of high-value tasks

How Scout Lit up the “Dark Side of the Moon”

Your business generates billions of data points from human-machine interactions. Scout, our AI model, deciphers this interaction data to unveil what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it's cost optimization, revenue growth, customer or employee experience, or business continuity.

The AI then provides data-based recommendations for the necessary interventions to address these challenges, paving the way for improved outcomes.

We call this lighting up the ‘dark side of the moon’.
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