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The Napier AML Maturity Model ©2 0 2 0 N A P IE R TE CH N O LO G IE S L I M I T E D | P R I VAT E & C ON F I D E N T I A L 1
Contents
Foreword 3
What is the Napier AML Maturity Model (NAMM)? 5
Analysis through the six lenses 6
Defining the stages of the maturity model 8
Lens 1: Strategy 9
Lens 2: People and culture 11
Lens 3: Process 13
Lens 4: Data 15
Lens 5: Analytics 17
Lens 6: Infrastructure 19
Conclusion 21
Getting to the next level 22
Who is Napier? 23
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Foreword
Today technology is rightfully recognised The entire approach to how technology The amount of money laundered Criminal behaviours are evolving
01 04
as a powerful means to solve some of the can enable efficiency and drive each year continues to increase, and remain a step ahead
biggest challenges in fighting financial results specifically for AML needs with reports of $3tn laundered
crime. deep understanding. globally in 2019
However, implementing technology into an The reasons for doing so are numerous: Regulatory bodies are applying Legacy technology is inefficient,
02 05
organisation comes with its unique set of increasing pressure on and in many cases is no longer fit
challenges. organisations to take AML seriously for purpose
by the way of issuing larger fines
Technology for technology’s sake is
no answer. Existing siloed approaches to AML Greater efficiency brings reduced
03 06
are not effective in fighting financial costs and reduces regulatory risk
crime or reducing the risk of being
fined by the regulator
Continued overleaf...
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FOREWORD
That’s why we designed the Napier We have helped our clients draw a road
How does the Napier AML Maturity Model (NAMM)
AML Maturity Model to help regulated map and implement technology to move benefit organisations?
organisations who are looking to upgrade them from varying stages of maturity
or replace their existing AML technology in AML to a proactive position which The NAMM has been designed to give damage, the stakes have never been
to identify current state and what best embraces state-of the-art technology, AI organisations a means to perform an higher to get AML right.
practice looks like. and real-time search. assessment of where their organisation
stands in terms of AML strategy There are two basic questions an
We have been in the unique position In putting together this document, it is our (specifically with regards to use of organisation should ask: do we know
of assisting our clients – from Tier 1 intention to share with you what we have technology), to monitor progress in what a successful AML programme
banks to fintech start-ups - to identify learned, and hope that you may find the improving maturity, and to implement looks like, and do we know the stages
and overcome these challenges. following insights valuable. benchmarks in order to identify of how to get there?
We understand many of the hurdles best practices.
associated with the successful The NAMM focuses on identifying the
implementation of technology to combat In a world where losing the fight against current level of maturity, and how to
financial crime. financial crime has huge consequences move your organisation to reach a
Julian Dixon for organisations, both in terms of leading state of maturity.
CEO monetary loss as well as in reputational
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INTRODUCTION
What is the Napier AML
Maturity Model (NAMM)?
The NAMM has been The maturity model follows a
4-step process:
designed to help
regulated organisations 01 02 03 04
understand the status of,
Assess the current situation Define a quantitative Identify the gaps Provide tangible steps
and continuously improve Assess and measure the target state Identify the gaps between the Provide tangible steps to move
their AML and compliance maturity of existing AML Define a quantitative target current state and the desired organisations from siloed AML
functions and practices state that can be achieved by state processes through to an end-
capabilities to move the
against best industry practice continuously improving the to-end client compliance view
organisation through to an as guidelines current functions, processes
optimal state for combating and systems
financial crime.
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OVERVIEW
Analysis through the
six lenses
In the figure overleaf, the The model sets out the following six high
level lenses, each broken down into a
model uses lenses and further five sub-categories:
dimensions to assess
and gain a deeper
understanding of an
organisation’s current
1. Vision and 2. People and 3. Process 4. Data 5. Analytics 6. Infrastructure
situation, and then strategy culture 1. Policies 1. Data Quality 1. Data Analysis 1. User interface
understand how to move 1. Vision 1. Culture and Leaders 2. Procedures and 2. Data Modelling 2. Sandbox and Impact 2. Accessibility
workflow Analysis (Search)
forward to an optimal 2. Strategic Planning 2. Functions and 3. Data Accessibility
3. Governance Organisation 3. Scenarios and Rules 4. External Data 3. Machine Learning 3. Auditability
position. 4. Performance 3. Team 4. Alert Management (Structured) 4. Dashboarding 4. Scalability
Management 4. Roles 5. Case Management 5. External Data 5. Reporting 5. Integration
5. Performance (Unstructured)
5. Knowledge Sharing
Tracking
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OVERVIEW
LENSES
VISION & PEOPLE &
PROCESS DATA ANALYTICS INFRASTRUCTURE
STRATEGY CULTURE
CULTURE &
VISION POLICIES DATA QUALITY DATA ANALYSIS USER INTERFACE
LEADERSHIP
DIMENSIONS
SANDBOX &
STRATEGIC FUNCTIONS & PROCEDURES & DATA ACCESSIBILITY
IMPACTS
PLANNING ORGANISATION WORKFLOW MODELING (SEARCH)
ANALYSIS
SCENARIOS & DATA MACHINE
GOVERNANCE TEAM AUDITABILITY
RULES ACCESSIBILITY LEARNING
PERFORMANCE ALERT EXTERNAL DATA
ROLES DASHBOARDING SCALABILITY
MANAGEMENT MANAGEMENT (STRUCTURED)
PERFORMANCE KNOWLEDGE CASE EXTERNAL DATA
REPORTING INTEGRATION
TRACKING SHARING MANAGEMENT (UNSTRUCTURED)
Figure 1. High level overview of the Napier AML Maturity Model
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OVERVIEW
Defining the stages of 5. LEADING
the maturity model • Proactive AML
function, centered
on clients
• KPIs and KRIs
The objective is to establish driving business
4. MATURE
where an organisation’s outcomes
• Machine learning • Highly specialised
vision, capabilities, 3. MANAGED
adopted to drive team focused on
processes and technology • Strong process- value (decrease investigations
2. IMPROVING based foundation risk and cost) • S
tate of the art
currently fit into the model. • Consolidated • Client-centric systems
• Reactive view of compliance
1. INITIAL rules based on • A
I and machine
• Key systems in risk policies • Optimised rules learning used to
This figure summarises the • O
ver reliance on place (TM, TS, etc.) continuously and scenarios drive work and
excel spreadsheets • Minimal workflow improved minimise risk
factors that constitute the • Rudimental rules
• End-to-end
• Inconsistent Data • K
PIs in place workflow and • R
eal-time search &
different levels of maturity in • Email workflows model and monitored automation investigations
organisations. Siloed View Client Centric
Figure 2. Moving from a siloed view to a client centric view
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THE SIX LENSES
Lens 1: Strategy
The most important element of In the context of the In the context of the
Napier AML Maturity Model, the optimal
building the strategy is taking a position for an organisation would be the
proactive stance, starting with the Leading Stage where a Client Centric AML
approach is adopted, and state-of the-
vision itself, and to ask the question:
art technology, AI and real-time search
functions are default.
“Where do we want to get to as
an organisation?”
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THE SIX LENSES
Lens 1: Strategy
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
Vision The AML functionis seen as Fulfil regulations and advise The AML functionis seen as Value-added collaboration Proactive - business partner Working together with the regulator to drive the change
reactive and mainly a ‘back- the business when specific core risk-mitigating function with the business to mitigate predicting new threats The organisation becomes a proactive business partner predicting
office’ function with limited risks are identified. Still supporting the business. risk and to improve customer before they emerge and drive new threats before they emerge, driving revenue through customer
business impact. mainly seen as a ‘back-office’ insights. revenue through customer insights.
function. insights.
Strategic Minimal planning defined only High level roadmap Plans to improve AML Strategic plans are defined 3-year plan, with quarterly Defining the roadmap to improve the AML function and budget for
for audit points and high-risk defined to implement and functions in place, budgeting to continuously improve the refresh, agile methodology innovation
Planning areas. Initiatives defined and deploy regulatory change and costing reviewed AML function; investments applied. Budget allocated for The organisation defines a 3-year plan for transformation which is
managed tactically. programmes. Bespoke local periodically with global & budgets allocated for innovations and research. reviewed quarterly. Agile methodology is adopted for implementation
initiatives in place. change function in place. improvements. and there is a budget allocated for innovations and research.
Governance High level roles and Governance for key processes Central governance Central governance driving Centre of excellence for Becoming a centre of excellence driving capital investments
responsibilities defined defined (e.g. first/second established and is the AML function globally. policies and system design. The organisation becomes a centre of excellence for policies and
but lack of consolidated line of defense), Roles and responsible for transforming Periodic interaction with Regulatory board to discuss system design. A Regulatory board is formed to discuss policies with
governance across areas and responsibilities documented. the AML function. regulators and business with regulators and innovate. regulators and drive innovation.
workstreams. driving the agenda.
Performance No KPIs and KRIs defined, KPIs and KRIs are defined KPIs and KRIs are in place KPIs and KRIs are in place and KPIs and KRIs proactively Becoming an organisation driven by KPIs and KRIs
Resources focus on closing at a high level. However no and are used to drive team periodically reviewed. Minimal managed. Metrics granularity KPIs and KRIs proactively managed. Granularity in metrics allows
Management alerts and cases. Inconsistent performance management performance forward and root cause analysis on poor allows for root cause analysis organisations to analyse the root cause of issues and determine how
performance across teams. process are in place. reduce risk. performing metrics in place. of issues and resolution. to resolve these.
Performance Performance is not tracked Performance metrics Performance metrics are Performance metrics are Performance metrics are Tracking performance for continuous improvement
across areas and teams. are tracked manually automatically tracked and automatically tracked with automatically tracked with Performance metrics automatically tracked with detailed dashboards
Tracking across teams with manual collated across teams for detailed dashboards. detailed dashboards and and analysts can drill down to specific areas. Organisations will need
performance summaries. management review. ability to drill down to specific to implement technology that has capability to produce the desired
areas. metrics in order to achieve automation.
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Lens 2: People and culture
Any program of change needs to To move an organisation to a place of AML maturity, there
are several further factors that need to be considered,
consider, as its top priority, how especially in view of AI being very much part of a
transformation will affect people and technological solution for AML maturity.
culture in an organisation.
Taking this into account, the most important goal
should be to minimise operational resources while
simultaneously maximising the talent in specialised teams
such as AI and data science teams.
One of the challenges worth noting about introducing
an AI capability is that talented data scientists with
experience in compliance and financial services can be
difficult to source. And therefore, it is prudent to plan for
this in terms of time and resources.
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Lens 2: People and culture
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
Culture & Command and control Risk management culture Objectives are defined Talent is managed across Empowering teams and Talent managed across functions, leadership
approach, micromanagement in place across teams and and shared across teams. functions and teams, high individuals to make the development programme
Leadership of tasks. Business objectives functions with clearly defined Initiative is rewarded and degree of collaboration across right choices. Leadership Empowering teams and individuals to make the right choices.
and knowledge is minimally objectives and strategy. team members empowered. teams. development programmes in Leadership development programme.
shared. place.
Functions & Each function within AML Functions are localised with Increased specialisation leads High degree of functional Functions share objectives Functions are centered around a holistic client view to support
is siloed and processes minimal standardisation of to improved collaboration specialisations (operational, and visions and clearly risk management
Organisation are often repeated across tasks and repetitions within between functions, on-shore investigative, etc.) supporting aligned with revenue Functions share objectives and visions; and clearly aligned with
teams. Nonstandard work per functions. vs off-shore in place to limit business functions. generating functions to revenue generating functions to support risk management.
location. costs. support risk management.
Team Local teams performing Minimal centralisation of Several centralised Limited operational No operational processes due Minimal operational processes, highly specialised teams
inconsistent processes. Lack teams for shared activities, processing units (e.g. to processes due to high level to AI & automation. Central No operational processes due to AI & automation. Central regulatory
of global guidelines. mainly localised teams. manage alerts), localised of automation and AI; teams regulatory strategy team, strategy team, automated investigations.
compliance team to manage focus on case mgmt. and automated investigations.
cases & investigations. regulatory change.
Roles Focus mainly on operational Minimal degree of Increased role specialisation: Decrease in operational roles, Focus on high value roles High value adding roles, data scientists, investigators
roles (e.g. alerts review). Lack specialisation, different types data engineers and data high degree of specialisation including data science (to Focus on high value roles including data science (to build new
of specialisation. Leverage of operational roles focus on scientist roles are present in and increased emphasis on build new models), analytical models), analytical investigation and data engineer positions.
external consultants. different parts of the process. the organization. investigative and analytical investigation and data
roles. engineer positions.
Knowledge Limited knowledge sharing Knowledge sharing limited Internal repository of Clearly defined processes Knowledge and best practice Knowledge sharing in KYC, transaction monitoring, AML
based on emails. to procedures and policies, good practice (processes, to share information and is shared, and periodic events Knowledge and best practice are shared, and regular events are
Sharing however stored on wword procedures, patterns) is knowledge gained on cases, are hosted to share findings hosted to share findings and achievements with the industry and
documents shared via emails. defined and knowledge is documents shared centrally. and achievements. peers.
shared.
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Lens 3: Process
The Client Centric approach is Introducing automation for routine and repetitive
tasks allows analysts to reallocate their time into more
underpinned by a focus on operational important tasks.
excellence and streamlined processes
The use of dynamic rules which are linked to the
which are driven by automation.
organisation’s policies help reduce false positives while
machine learning is used to increase efficiency.
Integrated case management across the client lifecycle
including monitoring, screen and review is fully
automated.
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Lens 3: Process
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
Policies Policies are defined at a high Policies are clearly defined with Policies are defined and Policies are centrally managed Policies centrally managed and Policies are continuously improved and proactively managed
level to ensure compliance with adequate depth and detail and centrally managed and stored in and reviewed regularly, with clear reviewed regularly. Interactions Policies are centrally managed and reviewed regularly. Interactions
current regulations. stored on word document with consistent formats. governance to manage updates with lawyers and regulators to with lawyers and regulators to add new policies.
inconsistent formats. and versions. add new policies.
Procedures & Procedures definitions are limited Limited uses of workflow, mainly Processes are clearly Processes are managed through 90% of the process is automated, High degree of process automation driven by AI
to word documents describing implemented on legacy systems. documented (e.g. BPMN) standard workflow applications; leveraging AI. Investigative 90% of processes are automated leveraging AI. Investigative
Workflow the steps required to perform a Limited ability to track processes. however the execution is spread Every process is tracked and processes managed on a processes are managed on a standard workflow platform.
process. No workflow in place. across multiple systems. process mgmt. is in place. standard workflow platform.
Scenario & Rules defined on spreadsheets Rules are hard-coded within Rules for monitoring, screening, Rules are managed within Dynamic rules-based Dynamic rules implemented resulting in minimal false positives
and macros that need to be system and high degree of IT etc. are managed within systems and key parameters can approach linked with policies. Dynamic rules, linked to the organisation’s policies, are
Rules manually updated on a monthly intervention is required to amend controlled applications and be amended by specialised users Machine learning used to implemented. Machine learning is used to increase the efficiency
basis with extracts of data. them. properly documented. with minimal IT intervention. increase efficiency and and effectiveness of scenarios.
effectiveness of scenarios.
Alert Reactive alert management Alert management process is Alert management based on Alert management process highly Alert management process Alert management highlighting true unusual behaviours with most
process triggered by static rules. managed on workflow, but high static risk profiles and thresholds. effective. Minimal amount of false focuses on high risk behaviours relevant datapoints
Management >99% false positives and high degree of false positives hinder Ability to manually tweak positives (THE SIX LENSES
Lens 4: Data
Getting data right is fundamental in Data comes in all forms and, in AML, it is becoming
increasingly important that a system can ingest
ensuring that an organisation has an unstructured and structured data, and that it makes that
optimal AML defense, and it is probably data accessible to users in real-time.
the biggest challenge that most
organisations face when transforming
their systems and processes.
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Lens 4: Data
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
Data quality Multiple, inconsistent data Multiple inconsistent data Standard data definitions, Data lake consolidating all critical Single data repository for Highest data quality standards with big data used as repository for
repositories with poor customer repositories holding information multiple systems holding data in single repository; however transactions and customers, all datapoints
and transaction data quality. High related to customers and different data, single version data is difficult to traverse due to high quality information, ability to Single data repository for transactions and customers, high quality
amount of spreadsheets. transactions. No spreadsheets of the truth, data cleansed and poor quality. access all data in real-time. Big information, ability to access all data in real-time. Big data fabric.
used for sensitive data. standardised. data fabric.
Data Modelling Limited definition of customer Standard data models are defined Standard data models and Standard data models in place, Standard data models in place, Standard data models across functions and single view of the client
and transactions inconsistent globally, however there is at least hierarchical structure are in ability to ingest additional data ability to ingest additional Standard data models in place, ability to ingest additional data using
across regions, business and 20% of inconsistencies in local place. Clear data taxonomy and using transformation tools data using intuitive data intuitive data transformation tools.
products. definitions. ontology. Data models applied requiring minimal IT support. transformation tools.
locally.
Data Data accessed and shared via Data is accessed via specific Data can be accessed on multiple Data can be accessed by multiple Ability to access the data based Data accessible in real-time by the right part of the organisation
excel spreadsheets shared via applications; however IT is often platforms but minimal controls on roles including data scientist on roles. Each user can see Ability to access the data based on roles. Each user can see subset
Accessibility email with limited security and required to provide extract of contents that are accessed. that can leverage data to build subset of data based on roles + of data based on roles + geography. Access is tracked.
controls. data. models. geography. Access is tracked.
External Data Limited use of external open- Use of bespoke commercial data Integration with high quality Integration with disparate data Use high quality structured data High quality structured data used to enhance investigations and
source data (e.g. OFAC lists) solution, with limited data quality. external risk data (e.g. DJ, sources to perform screening for screening, due diligence, and reduce false positives
(Structured) to enhance screening and Refinitiv) used on screening and and minimise false positives by investigative intelligence. Use Use high quality structured data for screening, due diligence, and
monitoring processes. monitoring processes. leveraging multiple datasets. external due diligence services. investigative intelligence. Use external due diligence services.
External Data Limited use of external data, Limited integration with Integration with unstructured Ability to access unstructured Ability to access articles, news Unstructured data to enhance screening and monitoring. AI used to
mainly via web searches (e.g. unstructured data repositories, data repositories (e.g. data repositories and process and media to enhance screening extract relevant info
(Unstructured) google) to run investigations. with searches performed MeltWater, Factiva) for search & information using machine and monitoring processes. Ability to access articles, news and media to enhance screening and
manually. investigation. learning on an ad-hoc basis. Minimise false positives. monitoring processes. Minimise false positives.
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Lens 5: Analytics
Just as getting data right is In the leading stage, the use of machine learning can
empower analysts and support analytics. When used
fundamental in configuring an correctly, machine learning models, both supervised and
optimal AML solution, having powerful unsupervised, can provide insights and context into client
behaviours that may otherwise not be detected.
analytics is critical for analysts to make
rapid interventions. Having good analytics dashboards puts the power of the
data at the fingertips of the analysts, giving them the
ability to slice and dice the data, drill down into the detail
and define and track KPIs with ease.
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Lens 5: Analytics
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
Offline Limited analysis performed on Key relevant data aggregated Data warehouse in place to Big Data repository allowing Ability to run offline analytics Detailed analytics available to provide client insights
excel spreadsheets. in access database. Standard perform offline analysis (month- to run multiple queries on on big data, run multiple Ability to run offline analytics on big data, run multiple aggregation,
Analysis queries provided. SQL knowledge end); predefined aggregation. aggregated data across multiple aggregation, drill down and drill down and analyse networks.
needed for analysis. Lack of granular drill-down. dimensions. Limited drill down. analyse networks.
Sandbox Impact analysis is usually Minimal automation on excel/ Standard tools are deployed in Current applications allow to Ability to run multiple what- Ability to run scenarios on dedicated sandbox
performed on bespoke tools (e.g. Microsoft Access based on the organisation to run what- tweak scenarios parameters if scenarios with a copy of Ability to run multiple what-if scenarios with a copy of production
& Impact SQL/Access databases). The macro. Process is mostly manual. if analysis; however, they are and test on data; however IT production data without impacts data without impacts on production system.
Analysis process is manual. mainly MS Access based. knowledge is required. on production system.
Machine No machine learning capabilities. Minimal statistical analysis Scripts deployed to focus Machine learning functionalities Supervised and unsupervised Machine learning models used to provide insights on clients and
deployed on bespoke scripts/ on specific areas (e.g. are embedded in systems and models, NLP, suggested actions transactions
Learning tools. Use of MATLAB/R to false positives detection, processes to detect anomalies and AI models that can be Supervised and unsupervised models, NLP, suggested actions, AI
perform offline analysis. behavioural analysis). Limited and automate complex analyses. amended by users embedded in models that can be amended by users embedded in systems.
productionisation. systems.
Dashboarding No dashboarding capabilities. Limited dashboard produced via Dashboard are generated on the Different dashboards available Dashboards available based on Ability to slice and dice data and provide bespoke analytics
excel pivot table and pivot charts back of the data warehouse/ based on different roles, however roles, drill down, ability to define Dashboards available based on roles, ability to drill down and define
with data manually uploaded on a systems. limited ability to drill down to bespoke KPIs per user and track bespoke KPIs per user; and track across multiple charts.
monthly basis. specific datapoints. across multiple charts.
Reporting Data is manually extracted Standard reports templates are Standard reports are available Standard reports are produced Standard reports automatically Unstructured data to enhance screening and monitoring. AI used to
from systems and manipulated available in excel with limited in each system with limited daily with exceptions and trends. produced; bespoke reports can extract relevant info
with spreadsheets to manually manual work required to update customisation. Additional manual Detailed monthly analysis be designed on the platform with Standard reports are automatically produced, bespoke reports
generate the required reports. data on a monthly basis. work required to enrich reports. automatically generated. limited effort. Pdf exports. can be designed on the platform with limited effort. Pdf exports
available.
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Lens 6: Infrastructure
Implementing technology to Choosing infrastructure that is highly scalable, that can
grow with increased volumes of data and transactions,
futureproof your organisation should gives your organisation a powerful foundation to
be at the forefront when developing build from.
an AML strategy.
Other considerations such as API integration, data sharing,
accessibility and auditability are all key to ensuring that
the technology can support a successful AML function.
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Lens 6: Infrastructure
INITIAL IMPROVING MANAGED MATURE LEADING BEST PRACTICE TARGET
User Inconsistent user experience Inconsistent user experience Multiple User Interfaces across User interfaces in the majority of Consistent UX designed around Applications support the analyst with state-of-the art user interface
on different systems. Not user across systems multiple systems, similar the systems are designed based specific users (compliance, data Consistent UX designed around specific users (compliance, data
Experience friendly, difficult to train users processes are performed on roles scientist, manager) based on scientist, manager) based on their processes.
and hinders process execution differently (e.g. case mgmt.) their processes
Accessibility Customer and transactional data Limited ability to access subset Data can be accessed directly by Real-time search on big data Real-time search on billions Ability to run searches in real time across multiple data points
can be accessed by emailing of data through the use of using specific systems and slices dataset; ability to access all data of data, real-time aggregation, Real-time search on billions of data, real-time aggregation, ability to
(search) query details to IT department specific systems. IT department of data can be downloaded on from a single repository. Queries ability to slice and dice data on slice and dice data on platform, pivot table.
that will extract offline involvement is often required xlsx for analysis are easy to define platform, pivot table
Auditability Limited audit logs available in All key processes are performed Technical audit logs are available Audit logs are mainly technical Real-time audit for all activities All decisions are auditable and the output of machine learning
systems or majority of processes via applications, however not all with different level of details in but cover all key processes performed on the platform by models explained
performed via email / outside of actions are properly logged and different systems. Difficult to performed by analysts and team users, ability to trace back to all Real-time audit for all activities performed on the platform by users,
audited tools monitored extract detail of granular tasks members data changes ability to trace back to all data changes.
Scalability Current system and infrastructure Limited manual processes, Only core platform are fully Systems are all scalable, however Platform is fully scalable with Highly scalable solutions used with optimised costs
not scalable due to manual however legacy solutions limit scalable, however costs are high cost to scale is high minimal costs for the business Platform is fully scalable with minimal costs for the business and
process, inefficiency and legacy the process and functional due to legacy components and enabling growth enabling growth.
solutions scalability
Integration Systems are integrated via flat Minimal integration between Data integration via batch API based interfaces and real API based integration with Highly integrated systems that allow data sharing between different
files or via manual upload of systems via batch / files. Several interfaces, reference data is fully time interfaces to transfer data different repository, modular areas
files, different processes are manual interfaces and data utilized in platform and systems across systems architecture, based on API based integration with different repository, modular architecture,
performed in different systems upload to extract and process microservices based on microservices.
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By moving to a leading Establish a proactive AML function Integrate traditional rule-based These functions give organisations the
through the use of technology and solutions with a sophisticated edge in the fight against financial crime,
position in the Napier AML drive business by minimising risk machine learning capability, while as well as helping to minimise risk and
Maturity Model, ultimately retaining the ability to explain and reputational damage.
Put clients firmly at the centre of the audit every decision
this gives an organisation
AML function - with transactions,
the ability to: onboarding, screening and reviews Give the compliance team the ability
all connected through state-of-the- to drill down into each transaction
art processes and systems and all relevant datapoints related
to clients
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Once you have identified Leaders can use this guide to identify the
specific dimensions of AML systems and
your organisation’s processes that should be given higher
current level of AML priority for improvement initiatives.
maturity, the model defines
Regardless of where your organisation
improvements to help you stands in its strategic AML maturity, if
move through the next you are interested in discussing how
technology can improve AML performance,
stages of maturity.
please contact us on info@napier.ai or visit
us on www.napier.ai
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Our solutions
Napier is a new breed of We have two solutions - one for Our products include: Our tools dramatically reduce both
Anti-Money Laundering (AML) and the • Transaction Monitoring false positives and false negatives and
AML and trade compliance other for Trade Compliance. Our solutions • Transaction Screening empower compliance teams to make
tech providers. can be applied to compliance challenges • Client Screening validated decisions with unprecedented
in any sector and address every aspect of • Client Activity Review speed and accuracy.
AML & TC. • Risk-based Scorecard
Our Intelligent Compliance We have an expert team driving forward
Platform is transforming All Napier products harness the power of The platform is extensible and scalable; our vision for compliance. With over 100
AI and can be integrated together into our developed specifically to adapt to the years combined experience in compliance,
compliance from legal
third-generation compliance platform. needs of businesses large and small, and IT and financial services, our expertise in
obligation to competitive importantly, industry challenges coming AML is second to none.
edge. ahead. It can be delivered via public
cloud, private cloud or on premise. To learn more about the Napier platform,
please visit www.napier.ai
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