Automation in AML: The changing landscape of the finance sector

Automation technology for the financial services sector

Fact: we live in a digital age.  Everything a financial institution needs to know about you is online and available for a fee. 

Gone are the days of having to visit a branch of your bank to open an account.  Everything is moving online. 

The number of bank account providers has greatly increased with the advent of online banks with no physical presence.  The demand for accounts has stayed static so companies are having to compete for a share of the market. In fact, one of the marketing tools being used is to promote how fast an account can be opened. 

Drivers for Change

The current generation of young adults have grown up living their life online. 

When it comes to purchasing services, the first place most people go is to a search engine.  They can change their energy supplier, mobile phone provider, insure their car, change their Sky package and renew their driving license while sitting at home watching the football on the 60-inch flatscreen tv ordered from Amazon and eating a Domino’s pizza ordered off the Dominos App!

Human interaction isn’t necessary anymore.  It’s seen as old fashioned, inconvenient and, worst of all, slow!  The biggest crime these days is not to provide a poor product; the biggest crime these days is to waste our customer’s TIME. 

Challenge

How do you remove humans from a process without compromising quality, introducing additional risk and maintaining control?

Benefits of Automation

New technologies have the potential to hugely impact anti-money laundering (AML) and counter financing terrorism (CFT) measures:

  • Removes human bias
  • Faster decision making
  • Account opening not restricted to office hours
  • Computers don’t get sick, go on holiday or have childcare emergencies
  • Better customer service
  • Cost savings

This isn’t just us saying it: the Financial Action Task Force (FATF) have previously stated “”new technologies have the potential to make anti-money laundering and counter terrorist financing measures faster, cheaper and more effective“.

Types of automation

Artificial Intelligence

AI is the science of mimicking human thinking abilities. AI uses advanced computational techniques to obtain insights from different types, sources, and quality (structured and unstructured) of data intelligence to “autonomously” solve problems and execute tasks. These tasks typically require human intelligence, such as recognising patterns, making predictions recommendations, or decisions. There are several types of AI, which operate with (and achieve) different levels of autonomy, but in general, AI systems combine intentionality, intelligence, and adaptability.

Machine Learning

Machine Learning is a type (subset) of AI that “trains” computer systems to learn from data, identify patterns and make decisions with minimal human intervention. It involves designing a sequence of actions to solve a problem automatically through experience and evolving pattern recognition algorithms with limited or no human intervention — i.e., it is a method of data analysis that automates analytical model building. Machine learning and NLP are the AI-powered capabilities offering the greatest benefits to AML/CFT for regulated entities and supervisors. Machine learning has the ability to learn from existing systems, reducing the need for manual input into monitoring, reducing false positives and identifying complex cases, as well as facilitating risk management.

Natural Language Processing (“NLP”)

Natural language processing (NLP) is a branch of AI that enables computers to understand, interpret and manipulate human language. Fuzzy logic is a logical technique that takes imprecise or approximate data and processes it using multiple values, in a way that produces a useable (but imprecise) output. Such logics are nonbinary, using a range of values instead of only 0 or 1. Fuzzy Logic systems can produce useful output in response to incomplete, ambiguous, distorted, or inaccurate (fuzzy) input, simulating human decision making more closely than classical logic, and extracting more useful information from data that is too imprecise to enable definite results to be derived using classical logic. Fuzzy logic can be implemented in hardware, software, or a combination of both.

Where can machine learning add value?

  • Identification and Verification of customers: In the context of remote onboarding and authentication AI, including biometrics, machine learning and liveness detection techniques can be used to perform micro expression analysis; anti-spoofing checks; fake image detection; and human face attributes analysis.
  • Monitoring of the business relationship and behavioural and transactional analysis:
    • Unsupervised machine learning algorithms: to group customers into cohesive groupings based on their behaviour. This will then create controls that can be set based on a risk-based approach (e.g. transaction threshold settings), allowing a tailored and efficient monitoring of the business relationship.
    • Supervised machine learning algorithms: Allow for a quicker and real time analysis of data according to the relevant AML/CFT requirements in place.
    • Alert Scoring: Helps to focus on a pattern of activity and issue notifications or need for enhanced due diligence.
  • Identification and implementation of regulatory updates: Machine Learning techniques with NLP, cognitive computing capability, and robotic process automation (RPA) can scan and interpret big volumes of unstructured regulatory data sources on an ongoing basis to automatically identify, analyse and then shortlist applicable requirements for the institution. They can also implement the new or revised regulatory requirements so regulated entities can comply with the relevant regulatory products.
  • Automated data reporting (ADR): the use of standardised reporting templates using automated digital applications (data pooling tools) making the regulated entities underlying granular data available in bulks to supervisors.

Key characteristics and benefits of a fully automated system

  • Written in one computer language: removing the requirement for complex interfaces between different systems
  • One supplier which means:
    • One contract
    • One set of terms and conditions
    • One instillation fee
    • One technical support team to handle all your needs
    • One renewal negotiation instead of several
  • Modular: so you can pick and choose which modules you want, and when
  • Faster: reduced human touch points results in a faster decision-making process when onboarding new customers
  • Cheaper: in most cases there are significant savings to be made in two main areas:
    • Firstly, the licenses for the automated systems are currently significantly cheaper than those for stand-alone systems. 
    • Secondly, staff costs can be decreased as the system takes overs task previously undertaken by a human.  In some cases, the cost in staff reductions may outweigh the cost of implementing the new system.
  • Automated Regulatory reviews: periodic review can be automated for Low and Medium customers within a client set risk framework freeing up staff to concentrate on High-risk customers and those Low and Medium risk that fail the risk framework. 
  • Automated Regulatory Reporting: as all your client data is in one system, the system can produce the Regulatory Data required by compliance as and when needed. 
  • Reduced risk of Regulatory sanctions: built in regulatory compliance with all required rules and regulations, and reduced reliance on human decision making (which can be inconsistent) coupled with accurate single-system generated Management Information allows companies to manage the risk of sanctions for non-compliance with regulations.
  • Reduced Backlogs: With more of the decision-making being undertaken by the system rather than staff, there will be a reduction in case management queues.

Why you should consider a fully automated system

  • Better customer experience
  • Increased customers
  • Reduced regulatory risk
  • Reduced costs
  • Increased staff retention
  • Ease of use

Path to Automation – How Project Partners can help

Project Partners can conduct a review of your current AML/Financial crime system landscape. This includes what you are currently paying for each system and how long you are tied to that provider.

In addition, Project Partners will also conduct a work-flow analysis to identify possible staff savings and highlight any staff areas that may be hit hard by automation. 

We then provide a five-year cost review showing what you will be paying if you make no changes to your set-up.

Project Partners provide you with at least two different cost comparisons using two different providers of fully automated systems to replace all the systems identified.

If you decide to change your system, Project Partners can fully manage the transition for you. It can take a number of years as old legacy systems reach the end of their contract.

Contact Project Partners today to book a discovery to provide an assessment of where you are and create a clear pathway to where you want to be.

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