Case Studies
DS as a Service - Churn Analysis and Alerting
DS as a Service - From idea to trade
DS as a Service - Next best offer recommendation engine
DS as a Service - Prospecting360/Soft Onboarding
DS as a Service - Strategic digitization platform
ESG as a Service - Case 1
ESG as a Service - Case 2
Regulatory as a Service - KYC/Onboarding and Fraud
Regulatory as a Service - Regulatory Single Source of Truth
DS as a Service - Churn Analysis and Alerting
Sponsors + Experts
- Head of Sales from a leading Swiss universal bank
Use Case Overview
The client aimed to drive transparent and achievable CRM by using our ferris.ai and creating bank-specific churn ontology within 3-6 months from the start of the use case.
Current Situation
- Worked an unstructured process
- When clients close their accounts or cancel their mandates, it usually does not come as a surprise to the RM
- Clients do or not sufficiently voice their issues and simply quit the relationship
- Process of creating transparency & driving changed highly manual
Desired Target State
- Worked an unstructured process
- When clients close their accounts or cancel their mandates, it usually does not come as a surprise to the RM
- Clients do or not sufficiently voice their issues and simply quit the relationship
- Process of creating transparency & driving changed highly manual
Impact
Revenues
Increased AuM of ‘turnaround’ clients by 25%
Quality
Enabled RMs to hold critical clients
Cost
Decreased churn-rate by 16%
Time
From idea to production within 3-6 months
Revenues
Increased AuM of ‘turnaround’ clients by 25%
Quality
Enabled RMs to hold critical clients
Cost
Decreased churn-rate by 16%
Time
From idea to production within 3-6 months
Core Agents / Solution Steps
- Bank-specific churn ontology
- Listen for softer and indirect signs of client
- Transparent and achievable CRM
DS as a Service - From idea to trade
Sponsors + Experts
- Head of Sales from a global private bank
Use Case Overview
The client is eager to drive their „house view“ communication broadly to all front-office staff and portfolio managers, in order to ensure the resulting research/advice leads to a trade/product sale. By utilizing our data science product ferris.ai, proposal/offer conversion rates were increased by 42% after an initial learning curve and algorithm calibration phase of 6 months resulting in additional AuM growth of 8% from targeted clients.
Current Situation
- The information flow from research or strategic asset allocation (CIO) to RMs and eventually to clients does rarely follow a structured path
- Risk managers were not good supported by portfolio managers and research
- It was difficult to explain why the timing or complexity, or risk of certain proposals were not a good fit for a specific client
Desired Target State
- The information flow from research or strategic asset allocation (CIO) to RMs and eventually to clients does rarely follow a structured path
- Risk managers were not good supported by portfolio managers and research
- It was difficult to explain why the timing or complexity, or risk of certain proposals were not a good fit for a specific client
Impact
Revenues
Conversion rate increased by 42%
Quality
Better alignment of Risk – PMs and RMs
Cost
AuM growth of target clients by 8%
Time
Results come up within 9-12 months
Revenues
Conversion rate increased by 42%
Quality
Better alignment of Risk – PMs and RMs
Cost
AuM growth of target clients by 8%
Time
Results come up within 9-12 months
Core Agents / Solution Steps
- Transparent and achievable CRM
- Bank communication is screened for gap/fit
DS as a Service - Next best offer recommendation engine
Sponsors + Experts
- Head of Sales from a European bank
Use Case Overview
The client asked for support to introduce a machine learning-based “next best offer” as a tool by using our ferris.ai. Besides, they wanted to define a sales process based on this and introduce a set of measurable KPIs for relationship managers dealing with clients.
Current Situation
- No automated analysis of client characteristics and history
- No common approach across sectors or companies
- No interface for relationship managers
Desired Target State
- No automated analysis of client characteristics and history
- No common approach across sectors or companies
- No interface for relationship managers
Impact
Revenues
Suggestion of personalized offers
Quality
Next best offer recommendation engine
Cost
Save +25%
Time
Continuous Tracking
Revenues
Suggestion of personalized offers
Quality
Next best offer recommendation engine
Cost
Save +25%
Time
Continuous Tracking
Core Agents / Solution Steps
- Successfully deployed the next best offer engine
- Constantly updated the recommendation model
- Recommend bespoke offer to customers
DS as a Service - Prospecting360/Soft Onboarding
Sponsors + Experts
- Head of Sales from a leading Swiss universal bank
Use Case Overview
The client would like to drive the idea of „soft onboarding“ instead of selling hard to a new prospect. We started to engage them with bespoke information or advice free of charge, alongside automating their CRM by leveraging our ferris.ai data platform.
Current Situation
- Hunting for new important clients usually is driven by referrals and the search for an „ideal event“ to introduce a bank’s services
- Existing client relationships are usually screened manually and approached directly to request an introduction, prior to offering any services
- Monitoring the market and a prospect’s connections can be cumbersome and is error-prone – either their introductions are awkward, or they do not focus on a specific and urgent need
- Success and conversion rates seem hard to plan
Desired Target State
- Hunting for new important clients usually is driven by referrals and the search for an „ideal event“ to introduce a bank’s services
- Existing client relationships are usually screened manually and approached directly to request an introduction, prior to offering any services
- Monitoring the market and a prospect’s connections can be cumbersome and is error-prone – either their introductions are awkward, or they do not focus on a specific and urgent need
- Success and conversion rates seem hard to plan
Impact
Revenues
Doubled the number of prospects
Quality
Allowed RMs to more advice-driven
Cost
Save +35%
Time
Reduced time ‘interest to closing’ from 12 to 7 months
Revenues
Doubled the number of prospects
Quality
Allowed RMs to more advice-driven
Cost
Save +35%
Time
Reduced time ‘interest to closing’ from 12 to 7 months
Core Agents / Solution Steps
- Strategic client team originally covered 200 prospects manually
- Propose the biggest impact levers
- Track progress over time
DS as a Service - Strategic digitization platform
Sponsors + Experts
- Head of Sales from a global private Bank
Use Case Overview
A European bank with global subsidiaries is keen to implement its digitization roadmap in line with the 2026 IT strategy.
Current Situation
- No digitization roadmap
- Requirements of 2026 IT-strategy
Desired Target State
- No digitization roadmap
- Requirements of 2026 IT-strategy
Impact
Revenues
Revenues of fertilizing the digital roadmap
Quality
Ferris.ai as the platform of choice (PoC)
Cost
TBD
Time
Ready to implement the 2026 IT strategy
Revenues
Revenues of fertilizing the digital roadmap
Quality
Ferris.ai as the platform of choice (PoC)
Cost
TBD
Time
Ready to implement the 2026 IT strategy
Core Agents / Solution Steps
- Reuse of Regulatory Data management effort as the base for the digitization 2026 roadmap
- Alignment of strategic digitization roadmap based on ferris.ai
ESG as a Service - Case 1
Sponsors + Experts
- Head of Sales from a Global Stock Exchange company
Use Case Overview
In Cooperation with Sustainserv a global worldwide leader in ESG consulting, we built an ESG ontology on our data science platform ferris.ai for this client. This ontology allows gaining structured information out of ESG Reports, where we can rate unstructured data between Level 0-5, automatization of manual research.
Current Situation
- Complex manual research
- Different ESG standards and materiality are in play
- Process of creating transparency & driving changed highly manual
Desired Target State
- Complex manual research
- Different ESG standards and materiality are in play
- Process of creating transparency & driving changed highly manual
Impact
Revenues
Increase ESG. Portfolio Value by 30%
Quality
Sustainability based consulting
Cost
Save +30%
Time
Very fast time-to-market
Revenues
Increase ESG. Portfolio Value by 30%
Quality
Sustainability based consulting
Cost
Save +30%
Time
Very fast time-to-market
Core Agents / Solution Steps
- Revolutionize client advice throughout automated rating
- Create new finance products
- No cumbersome manual research
ESG as a Service - Case 2
Sponsors + Experts
- Head of Sales
- Investment Committee/Decision
- Sustainability Team
Use Case Overview
The investment committee of the client needs to drive sustainable transformation and transparency about progress across a diverse portfolio of companies spread across sectors to mitigate the overall portfolio risk and opportunity.
Current Situation
- No or very different ESG strategies in place at portfolio companies
- No common approach across sectors or companies
- Different ESG standards and materiality are in play
- Process of creating transparency & driving changed highly manual
Desired Target State
- No or very different ESG strategies in place at portfolio companies
- No common approach across sectors or companies
- Different ESG standards and materiality are in play
- Process of creating transparency & driving changed highly manual
Impact
Revenues
ESG. Portfolio Inc. Value
Quality
Company Level Feedback
Cost
Save +30%
Time
Continuous Tracking
Revenues
ESG. Portfolio Inc. Value
Quality
Company Level Feedback
Cost
Save +30%
Time
Continuous Tracking
Core Agents / Solution Steps
- Acquire ESG data & benchmarks
- Evaluate sector materiality
- Align sector KPIs & drivers
- Assess sector & portfolio state
- Propose the biggest impact levers
- Track progress over time
Regulatory as a Service - KYC/Onboarding and Fraud
Sponsors + Experts
- Head of Sales from a global private Bank
Use Case Overview
The client has automated many aspects of background screening and adverse media monitoring by leveraging our ferris.ai technology and combining our domain knowledge that really required by law and regulation.
Current Situation
- Client struggles with the complexity and inefficiency of their client onboarding and recurring KYC monitoring practice
- Client has issues when it comes to AML, source of funds and transaction monitoring compliance
- The front-office staff often try to cut corners and the compliance staff is overwhelmed with the number of cases and follow-ups that are required from them
- Disintegrated and high-maintenance systems and processes are the usual status quo, with little budget and energy to change due to the inherent risk
Desired Target State
- Client struggles with the complexity and inefficiency of their client onboarding and recurring KYC monitoring practice
- Client has issues when it comes to AML, source of funds and transaction monitoring compliance
- The front-office staff often try to cut corners and the compliance staff is overwhelmed with the number of cases and follow-ups that are required from them
- Disintegrated and high-maintenance systems and processes are the usual status quo, with little budget and energy to change due to the inherent risk
Impact
Revenues
Increase auto-dismissal rate by 70%
Quality
Creating a centralized workbench
Cost
Reduced False Positive rate by >45%
Time
Track automation progress over time
Revenues
Increase auto-dismissal rate by 70%
Quality
Creating a centralized workbench
Cost
Reduced False Positive rate by >45%
Time
Track automation progress over time
Core Agents / Solution Steps
- KYC / Onboarding and Fraud Remodelling
- Automating standard case load
- Increased compliance quality and decreased client case resolution time
- Eliminating aspects not required by the current jurisdictional scope
Regulatory as a Service - Regulatory Single Source of Truth
Sponsors + Experts
- Head of Sales from a global private Bank
Use Case Overview
A European bank has semantically integrated +220 different data sources at Switzerland’s largest independent private bank. The regulatory team was able to deliver better results faster and yet decreased the team size by using our ferris.ai.
Current Situation
- Most regulatory solutions today require huge maintenance effort on both business and technology teams. Evermore granular and ever more near-time regulatory requirements further increase this pressure
- Usually the various regulatory domains have created and continue to create silos for central bank, credit risk, liquidity, ALM/ KYC and transaction monitoring regulations
- Further requirements from ePrivacy, product suitability and sustainability regulations even further dilute these efforts
Desired Target State
- Most regulatory solutions today require huge maintenance effort on both business and technology teams. Evermore granular and ever more near-time regulatory requirements further increase this pressure
- Usually the various regulatory domains have created and continue to create silos for central bank, credit risk, liquidity, ALM/ KYC and transaction monitoring regulations
- Further requirements from ePrivacy, product suitability and sustainability regulations even further dilute these efforts
Impact
Revenues
Induced annual savings of 15M CHF
Quality
Reuse of Single Source of truth for use cases
Cost
Reducing the cost of new use cases
Time
Increase usability and time-to-market
Revenues
Induced annual savings of 15M CHF
Quality
Reuse of Single Source of truth for use cases
Cost
Reducing the cost of new use cases
Time
Increase usability and time-to-market