The world of finance moves so fast with speed of light transactions how on earth can anyone counter financial crime? You’ve found the right place as we point to some detail to help you to fight financial crime and find the solution that meets your needs.
Preventing Financial Crime
We have written extensive mitigation controls to help our clients reduce the opportunity for financial crime occurring in their business. Talk to us to help you.
Improving Monitoring to Reduce Financial Crime
It’s not all about banking and transaction monitoring. The focus always goes to that. And yet the criminal knows it. Sophisticated criminals will get around transaction monitoring easily. You need to be smarter. Talk to us about how we can help you solve your issues.
Maybe the Solution is Internal?
Just take a look at the darkweb and you will see there is a volume of financial professionals working to clean dirty money and assets and selling their services to the highest bidder. We can route them out of your business. Forever.
Talk to us today to make your life less stressful – we have the financial crime solution for you.
Bring yourself up to date with this useful list of AML resources and help documents. We design training packages for your staff, the below is just a small section of our knowledge base. It is important to consider your requirement for bespoke training aligned to your risk.
Bring yourself up to date with this useful list of AML resources and help documents. We design training packages for your staff, the below is just a small section of our knowledge base. It is important to consider your requirement for bespoke training aligned to your risk.
FATCA – CRS Self certification – NB: We can help with FATCA/CRS reporting –
FATF guidance on Company formation agents and Trusts – A risk based approach to their work and the risk they face in the climate to remove hidden Beneficial Ownership. For a summary and the full report go here, or download the full report.
Bring yourself up to date with this useful list of AML resources and help documents. We design training packages for your staff, the below is just a small section of our knowledge base. It is important to consider your requirement for bespoke training aligned to your risk.
POLICY – An Anti-Money Laundering and Terrorist Financing Policy is the document that guides all AML activity and helps your organization guide staff. It is a critical document that should detail a lead from the top of the organization. Below we provide three institutional policies for you to peruse contrast and compare (the first is a Real Estate policy, the second/third are banking policies) . We make no comment on the quality. We provide this service for you to reassure you, your policy will meet the required regulatory rigour.
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AML/CTF Policies and Procedurestemplate – Seek our advice before using this. –
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Real Estate Policy Template. NB: Seek advice this is a guide only.
Risk Assesment and AML/CTF program audits – New Zealand –
Risk Assessment – BSA/AML Example – for a bank. We do not warrant the quality of this document. –
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Securities Exchange Commission Rules on Whistle-blowing
Guidance on Sanction Screeningfrom Wolfsberg –
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Standard for Automatic Exchange of Financial Account Information in Tax Matters OECD –
Template for AML program for a small firm – US centric NB- We do not warrant the quality of this document. You must seek our advice. –
Template for AML/CTF Policies and Procedures – Seek our advice before using this. –
Template forReal Estate AML/CTF Policy. NB: Seek our advice this is a guide only.
Terrorism. United Nations Convention for the Suppression of the Financing of Terrorism –
Transparency International Exporting Corruption Report –
Company Trusts. FATF guidance on Company formation agents and Trusts – A risk based approach to their work and the risk they face in the climate to remove hidden Beneficial Ownership. For a summary and the full report go here, or download the full report.
Big data is all around. It is said that in 20 years Facebook will have over 70,000 data points on our children. It seems to me that is perverse when the banking industry can’t yet match customer data within their own four walls.
With an industry bursting at the seams with data, yet no credible way to make use of it, it is time for a change of thought process.
Software available to banks to manage AML, be that the process to manage a case, or alert to a risk, is letting them down.
Institutes should be sharing data on customers and transactions automatically. This should be expanded to include public sector authorities also sharing data to provide a more holistic view of risk and criminality.
Currently all intelligence products focus on the same data-sets.
Adverse media
‘Some’ open source
Their own records
Fraud data-sets
Sanctions/watch lists
Imagine a solution that did all of that and included;
Social Media
Dark Net
Police Data
Full open source
Digital footprints
Tracking movement software
Biometrics
Psychographics
Port authority
Customs
Criminal data
Financial Industry intelligence network
and more…
Now imagine a system that utilised a sharing engine to control how the network ethically, lawfully and commercially shared data.
The police today work in a ‘plural’ policing network. Working with private companies, public authorities and security companies to keep us safe. They share data with all of them to prevent crime. And yet the banking industry doesn’t receive this data.
Add to these concepts an artificial intelligence solution that intelligently merges data-sets to create a product that provides less false positives and more meaningful and accurate data results. Shutting out the criminal with nefarious aims.
All of this is possible with today’s technology. A solution that can;
Data accuracy and relevance
Weight
Relevance
Accuracy
Timliness
Provenance
Grade
Dissemination rules
Utility
Score
To aid decision-making.
Operation Embrace is building this system. The team has over 200 years experience and critically, none of it is in IT with most forged from hard years at the front-line of AML. A team with a breadth of experience that includes military and policing intelligence experience.
The amount of revenue banks spend on AML is burgeoning. Hastened by tech that is letting them down. Rather than identify risk it fogs it with needless and inaccurate assumptions. How to reduce false positives is a critical question to save resource. Read on for some ideas…
One of the key ways this can be done is by improving Meta data management to help teams understand not only what the data is but where, when, why, how and by whom it was sourced.
From that an algorithm would be able to relatively easily grade and weight the accuracy and allow it to be merged with other equally assessed data. The problems stem from the below list.
Volumes of alerts, transactions and entity lists are growing.
“Sectoral” – focused sanctions on a specific area or activity versus blanket sanctions; these are here now are more are coming .
This combination creates more false positives than have been experienced in the past.
Poor data management, timeliness and accuracy cause inaccurate results.
Match Exclusion
Knowing not only what to include but just as importantly what not to, is a critical decision.
When is a match not a match
How you can meet issues such as sectoral sanctions
Focus here is on transaction screening but has lessons for customer screening
Reducing false positives
By introducing Artificial Intelligence systems, the hard work can be shifted to a machine, away from the highlighting pen of an AML investigator. While costs are burgeoning for human resource and accuracy is as low as 90% false positives, the decision is made, it is just time to implement it.
If we consider customer cost. Imagine being stopped and accused of shoplifting, what actually is the difference when we freeze accounts or cards pending investigation of a transaction?
False positive data
The matching of data across categories defines the problem. Poorly laid out matching rules with poorly managed data alerting to the wrong result.
Improving bank data
The below table gives some examples of poorly matched and alerted data. This gives rise to false positive results.
Comon false positive typologies
False Positive Impact
Time, money and morale are the three biggest issues with false positives. These three concepts amply demonstrate the criticality of improving false positive rates.
Cost of false positivesFalse positive distinction
By examining the nature of false positives, even with a human eye, we can categorise the main components causing the errors. Through this, focus can be made on the top 20%. The Pareto principle claims this will account for 80% of all errors. By improving this we improve the whole error issue.
Examine the data
If Machine learning is utilised to learn the patterns of errors, this step can be automated with supervised AI systems. This will reduce manual handling and improve accuracy. But the over-arching issue is to improve the management and accuracy of data with a governance structure throughout the business.
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