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USING AI TO DETECT MONEY LAUNDERING NETWORKS

MONEY LAUNDERING

By John Spooner, Head of Artificial Intelligence, EMEA, H2O.ai

 

Artificial Intelligence (AI) has evolved significantly from being a mere technology buzzword, to the commercial reality it is today.  The technology is making a positive impact across many industries, including the financial sector.  The financial services industry has a reputation of constantly innovating and advancing new technologies, in the pursuit of strengthening the customer base, and finding new revenue opportunities.  This is happening across all segments including capital markets, commercial banking, consumer finance and insurance.

The use of AI in the financial services is rapidly changing the business landscape, even in traditionally conservative areas.  According to a recent Bank of England survey of 500 UK financial institutions, two third

s of respondents were reported to have already been using machine learning in some form, with the median firm using live ML applications in two business areas.  This is expected to more than double within the next three years. Financial institutions today utilise AI for areas such as customer service, risk management, fraud detection and anti-money laundering, while adhering to regulatory compliance.

AI technology has proven to be reliable, especially when it comes to detecting money laundering, and is empowering leading financial services to tackle such issues in an increasingly effective manner.

 

MONEY LAUNDERING

John Spooner

Anti-Money Laundering

Money laundering is defined as “the concealment of the origins of illegally obtained money, typically by means of transfers involving foreign banks or legitimate businesses.” Reuters reported in 2017 that the total US and EU fines on banks’ misconduct, including anti-money laundering violations since 2009 amounts to $342 billion.

Money laundering poses a serious threat to the financial services sector.  According to the United Nations Office on Drugs and Crime, an estimated $2 trillion is “cleaned” through the banking system every year. Fines for banks that fail to prevent money laundering have increased by 500 fold  in the past decade, and is now worth more than $10 billion per year.  As a result, banks have constructed large teams, and allocated them the time-consuming tasks of identifying and investigating any suspicious transactions, which often takes the form of multiple small transfers within a complex network of players.

Traditional Approaches for Tackling Money Laundering

Typically, investigation teams use rule-based systems like FICOFiservSAS AML or Actimize to identify any suspicious transactions. This rule-based workflow consists of the following three steps:  Firstly, an alert is generated by the alerting system; secondly, the investigator reviews it using information from different sources and finally, the alert is approved as True Positive or classified as False Positive.   A False Positive can be defined as an error in data reporting, in which a test result improperly indicates the presence of a condition that in reality is not present.

However, the problem with rule-based systems is that they create a large number of false positives, usually in the range of 75 to 99 percent.  These means that a vast amount of time and manual effort is being wasted to investigate these false alerts.  The high number occurs because the rules can become outdated quickly and it take time for the systems to be recoded.

 

How AI Can Address False Positives

Anti-Money Laundering (AML) programmes that are used in capital markets and retail banking extensively deploy rule-based transaction monitoring systems, spanning areas across monetary thresholds and money laundering patterns. However, bad actors can adapt to these rules over time, and tweak their methods accordingly to avoid detection. This is where AI-based behavioural modelling and customer segmentation can be more effective, in discovering transaction behaviours and identify behavioural patterns and outliers, that indicates any potential laundering.

AI, especially time series modelling, is particularly effective at examining a series of complex transactions and finding anomalies.  Anti-money laundering using machine learning techniques are able to identify suspicious transactions, and also irregular networks of transactions. These transactions are flagged for investigation, and can be scored as high, medium, or low priority, so that the investigator is able prioritise their efforts. As the actors modify their behaviour, so does the AI that is underpinning the programmes, meaning the number of false positives stays low while maintaining a high number of true positives.

AI can also provide reason codes for the decision to flag transactions. These reason codes tell the investigator where they might need to search to uncover the issues, and help to streamline the investigative process.  AI is also able to learn from the investigators throughout the review, clearing any suspicious transactions and automatically reinforcing the AI model’s understanding and ability to avoid patterns that don’t lead to laundered money.

 

AI vs Rule-based Systems

AI-powered AML systems provide many advantages over an existing rule-based system.  This includes being able to dramatically reduce false positives, provide a curated set of alerts to the investigator and the ability to ingest domain specific IP customised for money laundering.  The AI technology can be strategically placed between the AML rule-based system and the investigator, which allows companies to gain a rapid return of investment.  Overall, the average investigation time is dramatically reduced from between 45 to 90 days, to mere seconds. It also greatly reduces any human inaccuracies and hours required per person, and can fit rule-gaps with innovative features.

Address Money Laundering and Drive Productivity

When used effectively, Artificial Intelligence (AI) can be a critical factor to success in the financial services industry.  It enables financial services companies to not only efficiently build personalised banking experiences, fraud and money laundering models but will also improve employee and business productivity.  As money laundering networks become ever more complex, the time is now, for progressive financial intuitions to start embracing AI in order to effectively combat money laundering, and to focus even more effectively on driving overall productivity.

Banking

HOW BANKING IS USING AI TO PROCESS CUSTOMER FEEDBACK

By Dan Somers, CEO of Warwick Analytics

 

More banks are turning to practical AI to rapidly analyse customer conversations for sentiment and emotional intent to get the insight and automation they need to transform their customer service and operations.

Here we look at 5 ways in which banks are using AI to process their customer feedback more effectively:

 

Processing incoming queries more efficiently

AI can remove the need for manual review of each incoming query and enables banks to handle them effectively from the outset.

The analytics can facilitate a much smoother omni-channel experience for the customer by: identifying which channels your customers are best suited to – and which work best for specific types of interaction; understanding the causes of channel failure and what drives customers to switch; and reducing customer effort by delivering service in the customer’s preferred channel first-time.

As a recent example, at one bank we were able to reduce the maximum time to respond to a customer from 3 weeks to 5 days. The solution used AI and machine learning to automatically analyse and prioritise all customer emails in near real time and routed high-priority cases to a dedicated work queue for fast action.

 

Automatically identifying customer intent and emotion

When different people are voicing different issues, they will use different words and sentiments. Vital data is often missed with traditional models and manual processes. For example a customer at a bank might say ‘by the time they called back, the bank was closed’. The keyword would be flagged as ‘closed’, when in fact the main issue was the call back. There are also other limitations with using just keywords such as sarcasm, context, comparatives and local dialect/slang. The alternative is to analyse text data using ‘concepts’ instead of ‘keywords’. This can be done effectively with AI.

 

Fast tracking customer complaints and issues

With AI you can send complaints straight to the relevant team for a faster resolution. We’ve helped banks reduce resolution time by up to 3 days which really boosts customer retention.

Dealing with specific complaints manually involves using more and more case handlers. Routing complaints automatically and prioritising by issue and category is also difficult due to the nature of complaints i.e. unsolicited, long and sometimes multi-topical. As a result, manual classification is often impossible within an acceptable time frame for the unhappy customer.

Using the latest AI however, banks are now automatically classifying unstructured data to provide an early warning of issues that need resolving fastest. This can lead to better and quicker outcomes at a much lower cost.

 

Spotting vulnerable customers early

Under the Financial Conduct Authority (FCA) front-line staff need to be able to spot different types of vulnerability in customers and support them accordingly. However, the volume of communication is just too much to carry this out manually.

The latest in AI speech transcription and text analytics is able to automatically detect hints at vulnerability from conversations with customers. The conversations are automatically analysed by to detect emotionally-driven comments that indicate vulnerability such as a basic lack of understanding, likelihood of a disability and circumstances. These vulnerabilities are flagged to the relevant members of staff for action. Regulated firms can also accurately understand the drivers behind the vulnerabilities so products, services and communications can be reviewed accordingly.

 

Banks using AI during Co-vid 19

During Co-vid 19 many banks have customer service agents working from home and/or in strict shifts. There has been a move from voice to webchat for many to cope with these changes which brings its own challenges and opportunities. Post-C19, many of these situations are expected to stay in place or at least not revert 100% back.

AI is helping to serve customers better focusing on taking cost out whilst keeping CSat up and channel switching down by improving chat optimisation, email, complaint handling and chatbot supervision.

 

Case study: Improving customer loyalty

A major UK bank was looking to improve its customer loyalty. It was already using the latest

analytical tools including social listening, sentiment analysis and a large data science team

but they were experiencing limitations and not making enough progress. They were also interested to see what online feedback their main competitors were receiving.

 

A number of key recommendations for the bank were identified using AI analysis:

  • A 10% increase in CSat (c. £200m pa revenue) from operational improvement
  • Comparable best-in-class churn e.g. Nationwide is 25% lower
  • Online and mobile banking is a key issue, and is causing direct churn
  • Drivers of churn are mostly customer service, branch closures, marketing offers, interest rates and vulnerability issues
  • Early warning can help predict churn tactically and intercept likely churners
  • 28% of Tweets and potentially all non-voice queries can be automated. This could be a £20m pa saving
  • Business banking, current accounts and ancillary services have the highest churn, and insurance the highest negative advocacy
  • Mortgages, current accounts, savings and overdrafts cause the most attritional set-up
  • There are distinct patterns and opportunities to adjust customer services resources to reduce churn and costs

With AI, this level of insight can be set up in a matter of days, delivered in near real time and without the need for a data scientist to maintain the model.

 

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Banking

BANKING’S SECOND WAVE OF TRANSFORMATION: INTEGRATING THE CLOUD-ENABLED FUTURE BANK

Keith Pearson, Head of Financial Services EMEA, ServiceNow

 

The last six months have seen significant changes to the financial services landscape, with operational resilience, economic recovery, cost reduction and an acceleration of digital transformation key themes emerging from the industry.

At the start of this crisis, much of the banking industry was in a different position to many businesses. The 2008 recession spurred a need for improvements and combined with the emergence of tech-savvy fintechs, the industry has seen a major shift as customer expectations have adapted. The pandemic has forced organisations to accelerate innovation already part-underway in the banking industry.

As banking experienced its first wave of transformation, institutions focussed on customer engagement, uniting physical and digital channels for an improved customer experience. Banks invested heavily in front office digital technology, creating visually appealing mobile apps, engaging online banking experiences and technologies for bankers to personalise customer engagement.

However, this digital engagement layer is not enough. Regulations like PSD2 reinforce the necessity to remain compliant, adding additional pressure to the digital transformation process which in turn has been accelerated by COVID-19. Banking is therefore in the midst of its second wave of transformation, where financial institutions are creating and seeking out critical infrastructure to better connect underlying middle and back office operations with the front office, and ultimately, with customers.

 

Keith Pearson

A disconnected operation

Many financial organisations are still struggling because they have yet to streamline, automate and connect the underlying processes that are enabling customer experiences. Which poses the question: why is connecting operations so difficult?

In most cases, multiple systems are still glued together by email and spreadsheets to track end-to-end status. Around 80% of a middle office employee’s time is spent gathering data from systems to make a decision, with only 20% spent actually analysing and making the decision.

The disconnect negatively impacts customers. For many, experiences like opening a bank account or getting a mortgage involve clunky, manual processes riddled with paperwork and delays. When front and back office employees lack the ability to seamlessly work together, customers can be asked for the same data multiple times, elevating frustration.

Customers have little patience and can be inclined to publicly broadcast problems when left unresolved. In a world of social media and online reviews, this could be detrimental to a company’s reputation.

With digitally native, non-traditional financial services players gaining market traction by offering a seamless customer experience, maintaining satisfaction is crucial for traditional banks to ensure that customers don’t switch. Banks must focus on making it easy for customers to do business with them by offering faster cycle times with more streamlined operations.

 

The fintech effect

Fintechs and challenger banks like Starling have shown what connected operations can do, having been built with digitised processes from day one. Modern consumers expect round-the-clock service from their bank. As financial institutions look to the future, developing a model of operational resilience that is capable of withstanding unforeseen issues, like power outages or cyberattacks, is critical to minimising service disruption. Having connected internal communications between front and back office staff means customers can be notified about any problems, how they can be fixed and when they might be resolved, as well as receiving continuous progress updates instantaneously.

Automation can go a step beyond this. Today, customers expect companies to not only do more and do it faster but to prevent problems arising in the first place. With connected operations and Customer Service Management (CSM), banks can proactively fix things before they happen and resolve issues fast, enabling frictionless customer service and replicating the ‘fintech effect’.

 

What about compliance?

In the European Union and the UK, PSD2 and the Open Banking initiative are giving more control to the customer over personal account data. Digital banks such as Fidor and lenders like Klarna are seeking to reinvent banking by offering customer-centric services. But the process of streamlining underlying operations is not simply about providing customers with the fintech-esque experience. More than 50% of a financial institution’s business processes are also impacted by regulation.

Financial services leaders are focussing on streamlining and taking cost out of business operations while also placing importance on resilience. Regulators are pushing banks to have a firmwide view of the risk to delivering their critical business services.

Banks must invest in digitising processes to intuitively embed risk and compliance policies, which are generally managed separately and often manually from the business process, leading to excessive compliance costs and risk of non-compliance. With the right workflow tools for monitoring and business continuity management, banks can minimise disruption by gaining access to real-time, actionable information about non-compliance and high risk areas, encompassing cybersecurity, data privacy and audit management.

Increasing openness of financial institutions to regtech solutions, or managing regulatory processes in the industry through technology, will prove key during this second wave of transformation. Banks will increasingly move away from people and spreadsheets and toward regulatory solutions that provide a real-time view of compliance and provide an end-to-end audit trail for Heads of Compliance, Chief Risk Officers and regulators.

With a unified data environment aided by technology, financial institutions can drive a culture of risk management and compliance to improve business decisions.

 

Riding the wave

The banking industry is still in the midst of its second transformation, and the pandemic hasn’t made it any easier. But riding this wave and successfully digitising processes to connect back and front office employees will present a profound difference to customer service.

The bank of the future will be frictionless, digital, cloud-enabled, and efficient; interwoven into the fabric of people’s lives. It will continue to be compliant and controlled but will deliver those outcomes differently, with risk management digitally embedded within its operations.

Demonstrating the operational resilience of its key services will not only drive customer confidence but will also provide a greater indicator of control to regulators and the market, adjusting overall risk ratings and freeing up capital reserves to drive more revenue and increase profitability.

The institutions that will thrive in this increasingly digital and connected world are the ones that are actively transforming themselves and the way they do business now, by taking learnings from fintechs, following regulations and paving the way in defining the future of financial services.

 

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