INSIGHTS AND OPINIONS FROM BARINGA PARTNERS · developed when regulators were more focused on...

Machine Learning for Financial Crime Professionals

INSIGHTS AND OPINIONS FROM BARINGA PARTNERS

Machine learning for financial crime professionals

Machine learning has the potential to revolutionise the current industry approach to Financial Crime. There has been a lack of innovation in Anti-Money Laundering (AML) and sanctions technology since it was first introduced at the turn of the century – for many organisations it is simply no longer fit-for-purpose. Machine learning offers the opportunity to identify financial crime more accurately and efficiently, whilst improving customer experience and reducing reputational risk. In order to harness this potential, financial crime professionals must develop a ‘working knowledge’ of this discipline.

Machine Learning for Financial Crime Professionals Machine Learning for Financial Crime Professionals

Overview

This article is intended to help make machine learning more accessible to financial crime (FC) professionals, especially those working in AML and sanctions. It discusses each of the topics described below in more detail to help establish a foundation for recognising the benefits of machine learning models (MLMs) within your organisation.

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Benefits and challenges of MLMs in FCMLMs offer a much more effective and efficient approach to FC compliance than traditional FC technology. However, it is important to recognise their limitations in order be able to use them successfully.

Why use MLMs? The ability of MLMs to learn how to identify FC means that they are able to efficiently capture the subtleties of criminal behaviour, which humans cannot easily codify into a set of rules. Furthermore, as we will discuss, there is now regulatory precedent for adopting MLMs to combat FC.

Benefits of MLMs over traditional FC controls The FC technology in place today was developed when regulators were more focused on ensuring controls were in place than determining how effective they were. Whilst the regulatory environment has moved on, the technology has not. Financial institutions (FIs) are now finding themselves using very blunt instruments to attempt to identify sophisticated criminal behaviour. Consequently, huge operations team are required to compensate for the inaccuracy of the technology.

Practical applications of MLMs in FC The ability of MLMs to model risk more accurately than rules-based techniques means that they can vastly improve the efficiency and effectiveness of FC controls. For example, they can provide incremental benefits within customer risk assessment, Politically Exposed Person (PEP) and sanctions screening, and AML transaction monitoring.

Key considerations There is much hype about MLMs, and for good reason, but they are not without challenge. We will discuss the following key considerations, which are important to understand before introducing MLMs into your organisation: i. Promises of near 100% accuracy will not materialise ii. Some vendors are starting to use a very broad definition of ‘machine learning’, which can be misleading iii. The better the data, the better the performance of the model iv. Domain and data science expertise are required v. It must be possible to explain the outputs of a model in order for it to be useful

Deploying MLMs in your organisationIn order to deploy MLMs successfully, it is important to develop the right knowledge and skills within your organisation. A robust model risk management framework should also be in place to ensure models are managed effectively throughout their lifecycle.

MLM basics Models are simulations of real world problems. The more accurately they represent the problem, the better they will perform. Machine learning techniques can create much more accurate representations of criminal behaviour than rules-based techniques:

‘Unsupervised’ learning can help to better identify similarities and deviations in behaviour which might be pertinent from a financial crime perspective

‘Supervised’ learning can much more precisely identify cases of FC by determining which aspects of customer behaviour were common to previous known cases of FC

Model lifecycle As models are representations of reality, they are never perfect. There is always a risk that there could be adverse consequences of placing reliance upon them – i.e. misinterpretation of FC risk. Model risk must be actively managed throughout the model lifecycle, which begins by clearly defining and documenting the purpose of the model. Models must then be developed, validated, used and maintained according to this definition.

Role of the Financial Crime Feature Engineer Effective FC models are dependent on deep FC domain knowledge as well as an understanding of the right data to present to MLMs to enable them to perform optimally. Working closely with data scientists, the ‘Financial Crime Feature Engineer’ plays a critical role in making MLMs operational.

With the right groundwork in place, MLMs have the potential to modernise FC controls and make them much more proportionate to the risk they are intended to mitigate.

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Benefits and challenges of MLMs in FC

Why use MLMs?With the prospect of driverless cars and queue-free supermarkets on the horizon, it is easy to see why many sectors are interested in the potential of MLMs. MLMs are certainly not the right solution for all problems but they do hold promise in FC. The problem of driverless cars is characterised by a vast range of inputs – such as other vehicles, pedestrians and road signs – which must be evaluated in order to determine the appropriate course of action – such as braking, steering and accelerating. It has eluded us for many years because formulating a set of rules capable of specifying which of these inputs should lead to which actions is too complex. MLMs are well suited to this type of problem (or family of problems, as the case may be) because they do not follow a set of rules. Instead, they learn how to translate the inputs into the required actions based on examples of similar situations.

FC also requires complex sets of inputs to be evaluated. Sophisticated criminal networks construct elaborate schemes in order to obtain and benefit from illicit funds whilst evading detection. Such schemes are present but hidden in the expanse of data captured by FIs. Creating an immutable set of rules, which accurately pinpoint instances of criminal behaviour within this data, is almost certainly impossible. They either fail to identify the illicit behaviour, or also identify a significant amount of behaviour that is not illicit – i.e. ‘false positives’. This is evident from high level of human intervention required to accurately identify FC using existing rules-based technology. Attempts have been made to implement a fully comprehensive set of rules within AML transaction monitoring,

for example, but the level of complexity required means the effort of attempting to maintain them outweighs the benefit.

MLMs are a good candidate for solving this problem because of their ability to identify the subtleties of criminal behaviour that cannot easily be codified into a set of rules. For example, a MLM may be provided with a range of customer records, their transactions and whether or not a Suspicious Activity Report (SAR) was raised against each of them. It can then be trained to automatically determine which customer behaviours led to SARs being raised.

Indeed, the business case for using MLMs in FC is very compelling, offering a much more proportionate approach to the problem than the over-sized operations teams currently required. They offer the following benefits:

Lower operational cost: MLMs are able to identify FC risk much more accurately, which means they generate fewer false positives, require less human intervention, and thus lower operational cost.

Better identification of criminal behaviour: Modelling criminal behaviour more accurately also provides the opportunity to identify FC risk that is currently being overlooked.

Reduced risk of reputational damage: Blunt rules can lead to poor decisions – e.g. exiting customers based on spurious links to terrorism. Well-trained MLMs can consider a wider variety of information and reduce the risk of inappropriate generalisations being made.

Better customer experience: Customer experience can be significantly disrupted by inadequate FC systems - e.g. customer payments being delayed. MLMs can help to ensure customers are only subject to additional FC checks where there are reasonable grounds for suspicion.

Another important reason for considering MLMs in FC is that there is now regulatory precedent for doing so. Only a year ago, FIs voiced hesitation about moving forward with MLMs in FC because of fears about the lack of regulatory acceptance of such approaches.1 Yet the UK’s Financial Conduct Authority (FCA) have since openly endorsed the benefits of machine learning2 and stated that it “…has the capability to better achieve what we all want: keeping finance clean.”3

There is clearly potential for use of MLMs in AML and sanctions and early adoption could lead to significant competitive advantage. Accordingly, HSBC and OCBC have already publicly announced their use of MLMs in this space.4

The UK’s Financial Conduct Authority (FCA) have since openly endorsed the benefits of machine learning and stated that it “…has the capability to better achieve what we all want: keeping finance clean.”

1. (2017) New Technologies and Anti-Money Laundering Compliance, Financial Conduct Authority, https://www.fca.org.uk/publication/research/new-technologies-in-aml-final-report.pdf.

2. (2017) From Maps to Apps: the Power of Machine Learning and Artificial Intelligence for Regulators, Financial Conduct Authority, https://fca.org.uk/publication/documents/from-maps-to-apps.pdf.

3. (2017) Using artificial intelligence to keep criminal funds out of the financial system, Financial Conduct Authority, https://www.fca.org.uk/news/speeches/using-artificial-intelligence-keep-criminal-funds-out-financial-system.

4. (2017) ‘HSBC turns to Google Cloud for analytics and machine learning capabilities’, Computer World UK, www.computerworlduk.com/cloud-computing/hsbc-turns-google-cloud-for-analytics-machine-learning-3655688.

(2017) ‘OCBC Bank is the First Singapore Bank to Tap Artificial Intelligence and Machine Learning to Combat Financial Crime’, OCBC Group, https://www.ocbc.com/group/media/release/2017/ocbc-bank-ai-and-machine-learning-to-combat-financial-crime.html.

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Benefits of MLMs over traditional FC controlsFirst generation FC technology was quite simplistic. To some extent, the presence of the following controls was considered sufficient to demonstrate compliance:

Customer Risk Assessment (CRA)

PEP and sanctions screening

AML transaction monitoring (TM)

However, the reliance of these controls on rules-based techniques mean that they are not very effective at identifying FC risk.

When they were first implemented, there tended to be a bias towards efficiency rather than efficacy. Rules-based techniques are quite blunt and, as such, often lead to a significant amount of false positives, miscategorising lower risk customers and behaviour as high risk. In order to manage the inflated workload this creates, FIs responded by artificially reducing the number of customers and instances of behaviour considered high risk. This helped to reduce the number of false positives but also increased the number of false negatives – i.e. customers and behaviour who genuinely are high risk being treated as lower risk.

Then, in the late noughties, when it was discovered that some FIs had been ‘wire stripping’,5 FC controls started to come under much greater scrutiny from regulators. The focus shifted from ensuring the controls were in place to determining how effective they were. This required FIs to recalibrate their systems to attempt to identify all customer behaviour that might be indicative of FC. In practice, this has meant loosening the thresholds used by the systems in place. This may have helped to identify additional cases of FC but it has also reintroduced the issue of vast quantities of false positives being generated.

MLMs are able to provide a more intelligent solution to this problem because they are much more precise. Rules implemented in FC detection systems are simplifications of behaviour considered to represent FC, generally based upon a limited set of data. MLMs can create much more accurate

representations of criminal behaviour by considering a broader range of data and identifying subtleties of such behaviour which humans cannot.

Consequently, MLMs are able to provide a much more sustainable solution to FC than traditional controls, dramatically reducing false positives and associated operational costs.

Practical applications of MLMs in FC

MLMs can provide incremental benefit to the rules-based techniques used by each of the controls mentioned previously. Take for example, PEP and sanctions screening. Following the wire stripping revelations, both payment and customer screening quickly became areas of ‘zero risk appetite’. To some extent, the vast quantities of false positives generated, and huge operational teams required to work them, have become accepted as the cost of doing business. However, there is an opportunity to take a much more risk-based approach. The risk associated with potential screening matches is highly variable for two reasons:

Inherent risk

Although some subjects on watch lists are clearly out-of-appetite for most FIs – e.g. individuals on locally applicable sanctions lists, the inherent risk associated with other subjects is much more variable – e.g. individuals in public offices which may not or may not be politically exposed.

Match quality

The accuracy, or quality, of the potential matches identified varies dramatically. This is because matching is often based on name. As name is rarely a unique identifier for individuals or entities, many inaccurate matches are generated. The use of approximate matching techniques (sometimes referred to as ‘fuzzy logic’) to overcome spelling differences and transliteration issues, further exacerbates this issue. By introducing a level of tolerance into the matching process, even more inaccurate matches are generated.

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5. ‘Wire stripping’ is the systematic removal of references to sanctioned targets from wire transfers so that they will pass through sanctions filters unnoticed

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This variability in risk is not effectively addressed by third party watch list providers. They provide increasingly larger lists of subjects without any risk prioritisation, or clear categorisation of subjects that might help to inform risk. Screening technology providers do not address it either as they provide no means of effectively managing the lists based on an organisation’s risk appetite.

A solution is required which effectively models the risk of subjects on watch lists upfront rather than waiting for investigators to do it once alerts have already been generated. Such a model would evaluate the following types of risk attributes, for example:

Inherent risk

Issuing jurisdiction of sanctions list: For example, FIs based in the US may have a different risk appetite towards US nationals on Russian sanctions lists than Russian-based FIs.

Prominence of public office: Some public offices are much more likely to pose a risk of political exposure than others – e.g. a member of the national legislature is likely to pose a greater risk than a local mayor. This is often relative to the jurisdiction in which the office is held.

Risk of corruption associated with the jurisdiction in which an individual holds office: Some jurisdictions have a much more widespread risk of corruption within public office.

Duration for which an individual has been out of public office: Although some individuals may continue to hold political influence long after leaving office, others – particularly where the role is less prominent – may cease to have influence quite quickly.

Match quality

Match strength: This may be measured in different ways – e.g. the Levenshtein algorithm can be used to determine how similar two words are in terms of spelling (e.g. ‘Tesco’ and ‘Tecso’) and the Metaphone algorithm can be used to determine how similar two words sound (e.g. ‘Derek’ and ‘Derrick’).

Name commonality: Some names are clearly more common than others – e.g. ‘Richmond’ is a much less common forename than ‘John’ within the UK. By considering how common a name is, it is possible to establish how likely the potential match is likely to be a true match within the population in which it was identified.

Corroborating factors: Other factors may be available to help confirm the identity of an individual. Such factors may be as simple as DOB or Nationality but they may also include occupation and known social relationships.

Potential screening matches that are both high risk and high quality are of much greater interest and priority to a FI. Conversely, low risk, low quality matches may be discounted entirely. It is very difficult to model the relationship between these two categories of risk using rules-based techniques. In addition to generating many false positives, they also incorrectly discount subjects of interest (i.e. they create false negatives), preventing them from being made available to investigators for review.

Rules-based matching techniques are quite blunt and, as a result, often generate a lot of false positives. Investigators must compensate for the inaccuracy of the rules by manually distinguishing them from true positives. Rules also often miss some behaviour of interest (false negatives), which means it is not available to investigators and goes undetected.

Inhe

rent

risk

Quality

Low risk, low quality Low risk, high quality

High risk, low quality High risk, high quality

True positive

False positive

False negative

True negative

Rules-based matching

Investigator decisions

MLMs can help to model the relationship between these variables much more accurately by exploiting subtleties in the data that cannot easily by captured by a set of rules. This means they are able to generate fewer false positives whilst identifying additional true positives.

Key considerationsWhilst there are many benefits, MLMs are not without challenge. There are five key considerations any FC professional should be aware of before attempting to introduce MLMs into their organisation:

Promises of near 100% accuracy will not materialise

As discussed, MLMs – in particular, ‘supervised’ learning models (see ‘MLM basics’) – are trained to identify criminal activity by presenting them with various examples, some of which are known cases of FC. They can be trained to identify the cases of FC in these examples very effectively and with a very low error rate. However, if their training is too specific to these examples, they will not be able to generalise their ‘understanding’ to different situations and the quoted error rates will be misleading. The accuracy of a model should not be based on its performance in relation to the examples it was trained with. Instead, performance should be based on a ‘blind test’ where outcomes are not made available to the model.

Not all ‘machine learning’ models are created equal

Given the hype around machine learning, it is not a surprise that many vendors of FC technology are keen to indicate that their technology uses MLMs. However, they are often using ‘machine learning’ in a very broad sense. A deviation in behaviour scenario in TM may rely upon some statistical analysis over time but that does not make it a learning model. Similarly, the Levenshtein algorithm mentioned earlier may help to compare natural language, but that does not put it in the same order as natural language processing (NLP) techniques which have recently come to the fore (e.g. those used by ‘home assistants’).

MLMs are led by example

Good example data is essential for training MLMs – in particular, ‘supervised’ learning

models (see ‘MLM basics’); without this, they will never perform as effectively. The range of examples presented to MLMs must:

Be diverse in nature: e.g. for TM, examples are likely to cover a broad range of different money laundering typologies for a variety of customer types

Contain a broad set of data for each example office: e.g. transaction, account, customer, KYC and reference data (such as country risk lists)

Have a clear outcome for each example: e.g. whether or not a SAR was filed

Accurate outcome data is very important for supervised learning, and can be a particular challenge within TM. Often the data about whether or not a SAR was raised is not easily available. Where it is available, it is not always possible to determine exactly which behaviour it was that was suspicious. Worse still, if the SAR was filed ‘defensively’, it may not have been suspicious at all. Proxies can be found where this is the case. For example, whether or not, after initial investigation, an alert was deemed ‘worthwhile’ – i.e. worthy of further investigation – could help to some extent.

Domain and data science expertise are required

MLMs are not ‘black boxes’ which FC professionals can simply plug-in and start using to suit their needs. Neither are they domain agnostic solutions that data scientists can deploy in isolation. As we will discuss, a critical role to establish is that of the ‘Financial Crime Feature Engineer’. This individual is responsible for determining the most appropriate data to present to an MLM to give it the best chance of identifying FC accurately.

Transparency is the key to success

A very effective model is of little use if the predictions it makes cannot be justified. There must be a clear rationale as to why a particular prediction has been made and this information must be readily available to FC analysts. Without this, the analysts will not have sufficient information to validate the prediction made and will not be able to evidence any subsequent decisions based upon it.

In comparison to rules-based techniques, MLMs are able to much more accurately represent risk and criminal behaviour. As a result, they identify many fewer false positives whilst also creating fewer false negatives. This greatly reduces the amount of human intervention required to identify FC.

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Inhe

rent

risk

Quality

Low risk, low quality Low risk, high quality

High risk, low quality High risk, high quality

True positive

False positive

False negative

True negative

Rules-based matching

MLMs

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Deploying MLMs in your organisation

MLM basicsA model is a simulation of a real world problem. It takes a series of inputs based on real world events and transforms them in some way to generate the outputs required.

For example, a coffee shop owner may want to determine how many staff to employ at different times of the day – this is the required output. In order to do this, the owner may create a rudimentary calculation (or model) based on the following inputs:

This model may act as a good ‘rule of thumb’ but it may not perform as effectively as the coffee shop owner would like, with too many staff at some times of day and too few at others.

MLMs may be able to provide a better solution to this problem. ‘Unsupervised’ models can be used to identify relevant patterns (or ‘correlations’) in the input data. For example, a ‘clustering’ algorithm may be used to identify logical groupings of different transaction types – e.g. transactions involving a large numbers of coffees, transactions involving coffees that take longer to make, and transactions where only one coffee is purchased. Knowing that such groupings exist may help to inform staffing levels required at different times of day. For example, there may be a higher number of distinct transactions at the beginning of the day but most of these may be single coffee transactions made by people on their way to work.

‘Supervised’ models use historic outcomes to help determine which combinations of the input data are the best ‘predictors’ of future outcomes. For example, the coffee shop owner may record whether there aretoo many, too few or the right number of staff to meet demand at different times of day.

Model

Outputs

ABC

Inputs

Average staff per

hour

Average number of

transactions per hour

Average number of

coffees per transaction

Average time to

make one coffee

= x x

OutputsInputs

Unsupervised model

Supervised model

OutputsInputs

Using this information, a supervised model can be trained to help predict future demand. For example, where sufficient data is available, it may also identify and utilise the weather forecast as an indicator of demand (e.g. fewer customers buy coffee when it is raining).

By using a combination of unsupervised and supervised techniques, the coffee shop owner can develop a model that is cognisant of trends in customer behaviour as well as the more subtle influences that affect demand.

These techniques can also be applied in FC. For example, unsupervised learning can be used to identify customers that ought to behave similarly (segmentation). This makes it easier to identify when a customer’s behaviour starts to deviate from their peer group in a way that might be indicative of FC. Supervised learning can be used to more accurately predict FC based on the attributes of a customer and their behaviour have been good indicators of FC in the past.

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Model lifecycleBefore these techniques can be deployed, models must first be developed and tested. A typical model lifecycle is as follows:

Reject

Define

1

Identifyappropriate

data

2

Design‘features’

3

Selectcomponents

4

Execute

5

Validate

6

Better thanalternative? Deploy

1 Define

Models must be clearly defined in order that we can have confidence they are performing correctly and that we can base risk decisions upon them. Model documentation must clearly set out the purpose of the model, how it ought to be used, the model’s design including any assumptions and known limitations, how it will be validated, and how it will maintained. This documentation should continue to be updated throughout the model lifecycle.6

Identify appropriate data

A model is only as good as the data it is based upon. Data quality does not need to be perfect but the better quality it is, the better the model is likely to perform. Careful thought must be given to which data to present to the model to enable it to learn and perform optimally.

Design ‘features’

Model performance is heavily dependent, not only on what data is presented to the model, but also on how it is presented. A ‘feature’ is a data point that has been pre-processed for use by a model. For example, ‘transaction amount’ might be used as a feature. If the transactions are in multiple currencies, the amount may first need to be converted into a common currency to enable the model to accurately evaluate this feature.

Select components

There are a wide variety of unsupervised and supervised algorithms that can be used in order to build an effective model. Data scientists must consider which algorithms are going to be most appropriate based on the nature of the problem and the quality of the data. During the model development process, several different components may be trialled before the most appropriate combination is identified.

Execute

Models must be executed and iterated until the results produced are considered appropriate for the defined purpose of the model. When developing a supervised model, is it important to split the data into multiple data sets in order that it can be trained and validated effectively. There ought to be at least one ‘training’ data set and one ‘validation’ data set. The training set includes both input data and outcome data in order that MLM can learn how to combine the inputs to effectively predict the outcomes. Once the model has been executed on training data, its outputs are compared to the actual outcomes. It may go through several iterations before its outputs are considered sufficiently similar to the actual outcomes. Care must be taken not to train the model on too narrow a set of data as it can become ‘over-fit’ – i.e. very good at predicting the outcomes in the training data, but unable to generalise these predictions to the new data it is presented with.

Validate

Validation of the model typically includes confirming the outputs (i) can be understood and explained, and (ii) can be corroborated based on the input data or other supporting evidence.

Once a supervised model has been trained, it must be validated using a data set where the outcomes are known but not made available to the model. This is referred to as a ‘blind test’. It is the model’s performance at predicting the outcomes in this new data that can be used to gauge how effective it really is.

Deploy

Generally, the performance of a model is compared to an incumbent model in place (at first, this may be a rules-based model) to determine how effective it is. If it is more accurate, a decision may be made to deploy the model into operational use. Otherwise, it may be necessary to revise the model until its performance improves.3

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6. See (2011) Supervisory Guidance on Model Risk Management, Office of the Comptroller of the Currency (OCC), https://www.occ.treas.gov/news-issuances/bulletins/2011/bulletin-2011-12a.pdf.

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Conclusion

As mentioned earlier, decisions regarding what data to present to a model and how to present it can make a significant difference to the effectiveness of the model. Indeed, this is often seen as more of an art form than a science. It is essential that the person making these decisions has strong domain expertise as well as a good understanding of data engineering. Within the FC domain, this is the role of the Financial Crime Feature Engineer.

This Engineer must determine which data is going to give the model the best chance of identifying FC. For example, in TM it is often not the individual transactions that make the customer’s behaviour interesting, but the customer’s transactional behaviour as a whole. Therefore, presenting a model with data at a transactional level is likely to give it a very narrow chance of success. Instead, the Engineer may decide that an aggregated view of transactional behaviour on a month-by-month basis is appropriate.

The Engineer must then create a set of features that summarise the most pertinent aspects of a customer’s behaviour from a financial crime perspective. Some of these might be quite similar to the inputs of common TM rules – e.g. summaries of the activity of the customer, and the customer’s segment, in the current month as well as the previous year. This is because these features have a strong potential of highlighting

atypical behaviour – it is the rudimentary way in which they are combined by rules-based models that often leads to inaccurate predictions. With MLMs, the key is understanding which features might be good candidates for predicting FC. The model can then determine what combination of these is most effective.

The Engineer must also be careful not to present data in too highly an aggregated form, as it may prevent the model from detecting what the true indicators of FC are. For example, simply presenting the output of TM rules to the model is unlikely to identify anything more nuanced than which rules are performing best. It will not be able to identify what makes those rules perform well, or how they can be made more accurate.

Existing FC technology is in need of modernisation. MLMs present the opportunity to make FC controls much more proportionate to the risk they are intended to mitigate. Investing in such techniques is becoming more palatable given recent regulatory endorsement. For regional and global FIs, the reduction in operational costs also make the business case for deploying MLMs in FC very compelling. Whilst this will require FIs to build out teams with the right skillsets – in particular, introducing the role of the FC Feature Engineer, FC professionals can develop a working knowledge of this discipline with relative ease.

Role of the Financial Crime Feature Engineer

It is essential that the person making these decisions has strong domain expertise as well as a good understanding of data engineering.

With MLMs, the key is understanding which features might be good candidates for predicting FC. The model can then determine what combination of these is most effective.

Copyright © Baringa Partners LLP 2018. All rights reserved. This document is subject to contract and contains confidential and proprietary information.

For further information, please contact:

Michael Lee, Partner, Data, Analytics and AI [email protected]

Richard Elliot-Cooke, Senior Manager, Financial Crime [email protected]

About Baringa PartnersBaringa Partners is an independent business and technology consultancy. We help businesses run more effectively, navigate industry shifts and reach new markets.

We use our industry insights, ideas and pragmatism to help each client improve their business.

Collaboration is central to our strategy and culture ensuring we attract the brightest and the best. And it’s why clients love working with us.

Baringa. Brighter together.

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