Financial & Transactional Analytic Solutions
Driving Financial Crime Detection Excellence - Your Partner for Data Analytics Expertise
Who We Are
Our Vision
A future where financial institutions are equipped with robust detection strategies and state-of-the-art tools to combat financial crime effectively.
We help clients on their maturity journey, leveraging our expertise to support organisations in navigating the intricate landscape of financial crime detection and prevention.
Our Expertise
With extensive experience in the financial services industry, led by David, we bring a wealth of expertise in financial crime data analysis and financial crime risk management.
From shaping financial crime detection strategies to spearheading operational teams and executing projects, our track record speaks for itself.
Our Approach
We believe in a collaborative & appropriately scaled approach to addressing your financial crime challenges.
We begin by gaining a deep understanding of our clients' specific requirements and objectives - for the immediate and longer term.
Through close collaboration and transparent communication, we work hand in hand with our clients to develop customised strategies and solutions.
Services
We can tailor an engagement to meet your particular needs. The below services highlight discrete offerings. Your particular needs may bridge one or more of these services.
In the below sections, we discuss detective controls in a general sense. Detection includes:
- Transaction Monitoring ("TM" - a component of ongoing Customer Due Diligence): behavioural detection of risks through a customer's interaction with a reporting entity's products
- Customer and Payments Screening ("CAPS" - a component of initial and ongoing Customer Due Diligence): an identification task of prospective or current customers in respective to certain risks - political exposure (bribery and corruption), sanctions, adverse media
We are able to support a range of delivery profiles, including:
- On-call SME inputs
- Methodological development
- Outputs/ work product reviews
- Direct delivery where we work alongside your teams
Detection Framework Maturity Review
Forming a view of your business' maturity is not an easy task. Bench-marking with peers is rare between Reporting Entities, owing to the required time and cost. What little bench-marking that is available is typically proprietary, the by-product of individual consulting firms selling the same work to reporting entity peers.
We approach this challenge using a standardised framework for self-assessment. We utilise a framework that describe the end-to-end capabilities necessary in a well-formed financial crime detective function. The framework describes each capability in terms of a maturity scale that can be used to independently assess a reporting entity's detective current state. This framework can be used to identify relative weakness in the current state, steering strategic resource investment to uplift detective maturity for the reporting entity.
Detection Strategy & Framework Development
Knowing how to target risks can be a challenge in and of itself. Be sure that you don't find yourself in a position where your business has acquired a vendor solution with "Detective Capability A", only to learn later that you you'll need "Detective Capability B" to address a particular risk.
Your detection strategy should be considerate of the Financial Crime risks relevant to your business (surfaced in your Risk Assessment). This strategy can bring together your plan to meet current and emergent Financial Crime risks through a range of detective technologies, as appropriate. Examples include:
- Primary detection methods: rules, scenarios, scorecards, learned models
- Secondary detection methods (triage): scorecards, learned models
Transaction Monitoring Risk Coverage Assessment & Scenario Gap Analysis
Scenario coverage is a common concern among reporting entities. We take a back-to-fundamentals approach in order to understand what financial crime risks your business is vulnerable to, and reflecting unacceptably high inherent risk, requiring active detective controls. From there, we review the conceptual soundness of existing risk lineage from the Risk Assessment through to detective controls (rules/ scenarios). If any of these elements are absent, we can support their development.
It's important to note that the presence of a mapped control (product of the gap analysis process) only demonstrates theoretical coverage. Measurement of the effectiveness of the controls (through a controls testing framework) is needed, which feeds back into the Risk Assessment to support view on residual risk.
Transaction Monitoring Scenario Design/ Development
We start by reviewing your current state for detection development, your documented processes, methods and governance. We can then assess against our standard method for detection development, providing structure from the requirements gathering stage, to indicators analysis, hypothesis development, and through to tuning & calibration. This method is scaled considering your business' resources.
Segmentation for Transaction Monitoring
Segmentation is an exercise in grouping customers that behave similarly for the purpose of Transaction Monitoring. Segmentation impacts Transaction Monitoring rules by improving the effectiveness of thresholds in identifying abnormalities in transactions (or transactional profiles) within a group that is behaviourally similar.
We can support your business with analysis, adjustment and validation of existing customer segments to best prepare your data for subsequent TM detection scenarios. This process by establishing a qualitative, business-led, view of divisions, products, channels and customers, which is then complemented by statistical assessment methods.
Tuning & Calibration of Transaction Monitoring Rules/ Scenarios
Tuning is often considered one of the most challenging aspects of operating a detective control. Understanding where to 'draw the line' often varies between stakeholders. A robust, demonstrable method that is reflective of risk appetite and contains feedback mechanisms can help to remove some of the challenges that arise from conflicting risk opinion than may reporting entities experience as they mature.
With can support you with the design & implementation of a right-sized method for the setting of quantitative thresholds and tuning through Above-the-Line ("ATL")/ Below-the-Line ("BTL") phases.
Management Information & Reporting
Monitoring & reporting on the performance of detective controls can often feel like a repetitive value-less exercise. It is common for reporting entities to think along the lines of workflow volumes - that is, number of alerts, cases, suspicious activity reports ("SARs"), PEPs/ Sanctions hits. Whilst these metrics are important, they often lack context and have difficultly informing strategy. For example, if the number of SARs for this month is 'x', and last month was 'y', then what does that mean?
Some reporting entities are tempted to frame detective performance in bands, tracking scenario productivity. This is problematic if the banding is driven by 'expert judgement'. For a given dataset, segment, scenario, parameter set, who is to say what is normal and abnormal with respect to performance?
Our method returns to fundamentals. We support the development of detection Key Risk Indicators ("KRIs") calibrated in the most recent tuning cycle. Ongoing monitoring of detection performance is then internally referenced within the detection component itself.
Detection Effectiveness Reviews
Simply operating a detective control does not in and of itself demonstrate compliance. If the control is ineffective in mitigating the desired financial crime risk, then it will be difficult to demonstrate its benefit to the reporting entity risk assessment.
Detection effectiveness is typically established with favourable outcomes from two process frameworks: the quality framework (control/ assurance and the controls testing framework (design and operating effectiveness).
We can review of your controls testing methodology and outputs, providing a technical view of the design and execution of the design and operating effectiveness tests.
Analytics Training
Saying financial crime analytics is a niche area is arguably an understatement. Many reporting entities struggle to find and retain experienced and knowledgeable team members, particularly those with an end-to-end understanding of risk frameworks and governance structures (the 'big picture').
We can tailor training content to your particular needs, considering the size and experience of your teams. From technical, methodology guidance, through to masterclass sessions on core topics like Segmentation and Tuning, we can support your learning and development goals.
About
David Coppin
David is an experienced Financial Crime Data Analyst and Leader, with a demonstrated history working in the financial services industry, contributing to financial crime strategy, operational teams and project delivery. Skilled in data analysis (SQL, python), detection modelling, detection systems, requirements definition, team leadership, mentoring & development, and stakeholder engagement.
David maintains strong financial crime subject matter knowledge, as a Certified Anti-Money Laundering Specialist (CAMS). David's work experience has focused on AML/CTF and Sanctions risk management and data analytics, including the development, tuning and validation of detective systems (TM, Customer and Payments Screening).
Industry experience includes a range of domestic reporting entities and international banks, from internal 1st/ 2nd Line Risk roles through to consultancy.
Technical experience includes industry leading Transaction Monitoring, Customer and Payments Screening detection systems, such as:
- Symphony AI NetReveal
- Oracle FCCM
- IBM Safer Payments
- Accuity Global Screening
- Dow Jones Risk and Compliance/ Factiva
- Refinitiv World Check
- Comply Advantage
- Innovative Systems RiskScan
David has broad interest in Financial Crime detection, with particular interests in databases, graphs and network analytics, machine learning for detection scoring, large language models and clustering analysis for customer segmentation.
David's technical skill set includes MS Excel/Access/VBA, Python (pandas, sklearn, seaborn), SQL (SQL Server, PostgreSQL, Teradata), PowerBI & Tableau.
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