Financial institutions must grow and serve customers while managing fraud, conduct, credit, liquidity, operational and model risk. We help teams make those trade-offs visible and improve the decisions embedded in products, controls and customer journeys.
Marketways’ analytical approach
Marketways combines sector understanding with an appropriate quantitative model. Depending on the decision, the model may draw on statistical, econometric, forecasting, simulation, optimisation, machine-learning or AI methods. We test the model against evidence and connect its result to an action and a measurable business outcome. Explore our methods and technologies.
What Marketways finds distinctive about this industry
- Transactions happen at high volume and low latency, while errors can carry regulatory and customer consequences.
- Customer, account, channel and counterparty relationships matter more than isolated events.
- A model that ranks risk does not replace policy, investigation or accountable judgement.
- Digital growth changes service demand, fraud patterns, operating controls and inclusion.
Where we work in the organisation
- Retail and corporate banking
- Risk and compliance
- Payments and operations
- Insurance and claims
- Customer experience
- Data, technology and model risk
Projects Marketways undertakes
Adaptive AML risk intelligence
How Marketways helps: We answer which combinations of behaviour and relationships warrant investigation. Using appropriate research and analytical methods, we reduce low-value alerts while surfacing contextual risk.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Rules remain necessary, but isolated thresholds can miss patterns and overwhelm investigators.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Transactions: establishes the amount, timing and mix of work the organisation must serve.
- Customers: shows which groups create different needs, demand patterns, risks or responses, so an average does not hide material differences.
- Entities: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Geography: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Alerts: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Investigation outcomes: records the business or service result against which the proposed change is judged.
How we judge success: Useful alert yield, missed risk, investigation time and explainability.
Related services
Risk Detection, AI & Model Risk.
Supporting methods
Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation.
Customer churn and relationship value
How Marketways helps: We answer which customers are at risk, why, and what response is worthwhile. Using appropriate research and analytical methods, we focus retention action on relationships where intervention can create value.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Propensity to leave, value at risk and responsiveness to an offer are different questions.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Transactions: establishes the amount, timing and mix of work the organisation must serve.
- Products: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Interactions: shows how customers, work or assets move through the system rather than relying only on a final total.
- Complaints: identifies where service, reliability or control breaks down and makes the consequence of failure measurable.
- Tenure: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Campaign response: records what the organisation changed, allowing the analysis to distinguish an intervention from the conditions around it.
How we judge success: Incremental retention, relationship value and contact cost.
Related services
Customer Satisfaction & Experience Research, Operational Performance Diagnostic.
Supporting methods
Machine Learning & Predictive Analytics, Market, Customer & Behavioural Analytics.
Payment failure and fraud journey
How Marketways helps: We answer where do legitimate payments fail and suspicious payments pass. Using appropriate research and analytical methods, we improve customer completion and control effectiveness together.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Stronger friction can reduce fraud while also excluding legitimate customers or shifting fraud elsewhere.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Payment events: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Authentication: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Declines: identifies where service, reliability or control breaks down and makes the consequence of failure measurable.
- Disputes: identifies where service, reliability or control breaks down and makes the consequence of failure measurable.
- Devices: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Merchant context: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
How we judge success: Fraud loss, false decline, completion and review time.
Related services
Process & Workflow Analysis & Redesign, Risk Detection.
Supporting methods
Process, Workflow & Systems, Machine Learning & Predictive Analytics.
Credit and collections decision review
How Marketways helps: We answer which applicants or accounts need which decision or treatment. Using appropriate research and analytical methods, we improve risk-adjusted approval and recovery decisions.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Historical outcomes may reflect past policy and unequal access rather than underlying ability alone.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Applications: establishes the amount, timing and mix of work the organisation must serve.
- Bureau data: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Cash flow: connects the operational pattern to affordability, commercial value and the financial consequence of the decision.
- Repayment: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Treatment: records what the organisation changed, allowing the analysis to distinguish an intervention from the conditions around it.
- Outcomes: records the business or service result against which the proposed change is judged.
How we judge success: Risk-adjusted return, cure rate, fairness and decision stability.
Related services
Decision Assurance, AI & Model Risk.
Supporting methods
Statistics & Econometrics, Machine Learning & Predictive Analytics.
Branch, contact-centre and digital capacity
How Marketways helps: We answer how should service capacity change as customers move between channels. Using appropriate research and analytical methods, we reduce avoidable waiting and preserve support for complex needs.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Digital adoption does not remove demand; it changes contact reasons and escalation patterns.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Visits: establishes the amount, timing and mix of work the organisation must serve.
- Calls: establishes the amount, timing and mix of work the organisation must serve.
- Digital journeys: shows how customers, work or assets move through the system rather than relying only on a final total.
- Handling time: makes delay, duration and timing visible because the same volume can require a different decision when it arrives or moves differently.
- Abandonment: identifies where service, reliability or control breaks down and makes the consequence of failure measurable.
- Staffing: represents the people, assets or resources available to meet demand and the constraints that limit the response.
How we judge success: Completion, waiting, first-contact resolution and cost to serve.
Related services
Workforce Planning & Capacity, Process & Workflow Analysis & Redesign.
Supporting methods
Forecasting, Risk & Optimisation, Process, Workflow & Systems.
Insurance claims triage and leakage
How Marketways helps: We answer which claims need straight-through handling, expert review or investigation. Using appropriate research and analytical methods, we shorten valid claims while concentrating expertise on uncertainty and risk.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: A rigid triage model can delay unusual but valid cases and obscure systematic leakage.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Claims: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Policy terms: defines what the recommended action must respect before it can be considered feasible or safe.
- Documents: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Repair/provider data: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Outcomes: records the business or service result against which the proposed change is judged.
How we judge success: Settlement time, leakage, referrals and overturned decisions.
Related services
Risk Detection, Process & Workflow Analysis & Redesign.
Supporting methods
Machine Learning & Predictive Analytics, Process, Workflow & Systems.
Liquidity and balance-sheet stress scenarios
How Marketways helps: We answer how resilient is the institution under linked market and behavioural shocks. Using appropriate research and analytical methods, we expose concentrations and management choices before conditions deteriorate.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: Depositor, borrower and market reactions can change together, invalidating single-factor stress tests.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Cash flows: connects the operational pattern to affordability, commercial value and the financial consequence of the decision.
- Maturities: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Behavioural assumptions: shows how customers, work or assets move through the system rather than relying only on a final total.
- Market factors: captures market forces outside the organisation that can change demand, performance or the attractiveness of an option.
- Limits: defines what the recommended action must respect before it can be considered feasible or safe.
How we judge success: Buffer adequacy, limit breaches and decision triggers.
Related services
Risk & Uncertainty Modelling, Decision Assurance.
Supporting methods
Forecasting, Risk & Optimisation, Statistics & Econometrics.
Model inventory and reliance review
How Marketways helps: We answer which models influence material decisions, and can the institution rely on them. Using appropriate research and analytical methods, we prioritise validation and controls according to real business consequence.
How we frame the analysis: We define the outcome and select the evidence and model needed to test the decision.
What makes the sector context important: A technically accurate model may still fail through poor data, workflow use or changing conditions.
Evidence we examine and why it matters
We examine these variables as a connected explanation of the business problem. Each variable has a specific role in the analysis:
- Model purpose: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Inputs: is examined to determine whether differences in this factor alter the business decision, the result or confidence in the analysis.
- Validation: tests whether the analytical result is reliable for the intended population, decision and operating conditions.
- Overrides: tests whether the analytical result is reliable for the intended population, decision and operating conditions.
- Monitoring: tests whether the analytical result is reliable for the intended population, decision and operating conditions.
- Incidents: identifies where service, reliability or control breaks down and makes the consequence of failure measurable.
How we judge success: Coverage, unresolved findings, drift response and controlled use.
Related services
AI & Model Risk, Business Systems Design & Architecture.
Supporting methods
Data Foundations & Business Intelligence, Machine Learning & Predictive Analytics.
Case studies combining several projects
Marketways combined related projects when the engagement required evidence, decisions and implementation to be connected across functions.
Payment Journey and Fraud-Control Redesign for a Local Bank
Marketways combined these projects to connect payment completion, fraud, operations and customer impact.
- Payment failure and fraud journey
- Adaptive AML risk intelligence
- Branch, contact-centre and digital capacity
- Model inventory and reliance review
Engagement shape: Multi-quarter transformation. Success was assessed through: Safer growth with fewer avoidable failures and clearer control.
Customer Retention and Credit Decision Analytics for a Regional Financial-Services Company
Marketways combined these projects to coordinate retention, credit and service treatments around relationship economics.
- Customer churn and relationship value
- Credit and collections decision review
- Branch, contact-centre and digital capacity
Engagement shape: Initial diagnostic and treatment trials. Success was assessed through: Incremental value, fair treatment and sustainable service cost.
Published case studies connected to this industry
These links point to existing published Marketways case studies. Each case may combine several project areas.
- Real-Time AML Risk Intelligence Using Bayesian Networks
Connected project areas: Adaptive AML risk intelligence.
- Predictive Modelling for Customer Loyalty & Churn Management
Connected project areas: Customer churn and relationship value.
- Foreign Exchange Branch Demand Analytics for a Regional Money-Services Provider
Connected project areas: Branch, contact-centre and digital capacity.
AI agents and workflows in this industry
These application notes explain where controlled workflows are sufficient, where an AI agent adds value and how the operating design changes in this industry.
AI Agents in Banking, Finance and Insurance: Use Cases and Implementation
AI agents in banking, finance and insurance should own a bounded operating goal, not an unlimited business function. This application note shows a credible use case, the evidence the agent needs and the decisions that remain with people.
AI Workflow Automation in Banking, Finance and Insurance: A Practical Design
An AI workflow combines defined process steps with selected AI tasks. In banking, finance and insurance, the strongest designs automate evidence handling while keeping material judgement and authority visible.
Discuss an industry question
A useful conversation can begin with the decision, operating context, evidence already available and the consequence of getting the decision wrong.