Konstantin Bodragin, Business Analyst and Digital Marketing Officer at Bruc Bond
Over the last couple of decades, AML has taken centre stage in the banking world. Nowadays, AML, shorthand for anti-money laundering, drives strategic planning and organisational structuring. AML concerns keep many a manager up long into the night, as the risks are huge, the penalties for infractions potentially devastating, and the criminals – especially in the era of COVID-19 – ever more enterprising. While the prevention of money laundering is paramount, the weight and risk faced by financial institutions may feel onerous to many. Luckily, the banking landscape is changing rapidly, with automation and AI making the burden significantly lighter to carry.
Banks and financial institutions face a two-pronged problem. On the one hand, the pace of digital payment is growing exponentially. Much of the world’s trade is now conducted through purely digital conduits. But it’s not only the volume of digital payments and users growing, so is the speed of transactions, with instant payment systems being deployed around the world.
The increases in speed and volume are of course good news for the bottom line, but require significant resources to handle effectively. Resources that many in the banking industry are struggling to provide adequately. The industry is shrinking rapidly, with bank closures, mergers & acquisitions, and a massive reduction in the workforce dominating headlines in the last decade. COVID-19 has only accelerated the trend, with bank after bank announcing imminent layoffs and reductions in trading. With the squeeze on resources, many banks would have struggled to keep up with the increased workload regardless of any other constraints, but here they are faced with the second prong: the complexities of AML.
AML regulations have grown thick and convoluted in recent decades, and with penalties as severe as truly massive fines and personal liability for offending compliance officers, it is taken extremely seriously. And for good reason. Fraudulent and criminal activity is costing the global economy many billions each year, with the lighter end of the spectrum meant to merely enrich the perpetrators, while at the other lies terrorist financing and socially damaging criminality. Nevertheless, it is a significant strain on banks’ already constrained resources, directly at odds with the growing pace of global digital trade.
To alleviate these pains, bankers and financiers of all varieties are scrambling to adopt the newest technologies to combat money laundering effectively, efficiently and with minimal costs. For this, AI seems to be the answer, and everybody wants a piece of the action. In 2020, you would struggle to find a fraud prevention company that doesn’t have the words ‘AI’ or ‘machine learning’ somewhere in its description.
Machine learning, one of the tools underpinning the AI fight against fraud, means the use of algorithms and statistical models to allow computers to perform tasks without specific instructions. In the context of payments, this means allowing computers to make decision related to AML compliance with no human intervention. While letting go of control is a scary prospect for many a financier, it may be the only right thing to do for effective AML implementation, both to prevent money-laundering incidents and to reduce the rate of false positives.
Current statistics indicate that for every fraudulent transaction stopped by a bank’s compliance team, some 20 legitimate transactions are prevented from going through by understandably overcautious compliance officers. Not only does this represent a serious hit to the bank’s bottom line, it wastes whatever precious resources are at the team’s disposal.
With current, manual methods, any suspicious transaction needs to be investigated in a process that can take anywhere from an hour to several days or weeks, often requiring the input of numerous team members and stakeholders across several departments. The cumulative resource drain is palpable, and the end result is that transactions are often rejected not due to any illegality, but because it is simpler, quicker and cheaper to do so. It is simply easier to suspect everyone and reject transactions outright. With AI systems, this process can take an entirely different shape.
Machine learning algorithms learn from human behaviour, create and continuously improve user profiles and use this information to validate transactions. Where this technology shines are with onboarding and transaction verification. Or rather, whenever a known user’s identity needs to be verified. A distinct change in a user’s behaviour is serious cause for alarm and indicates potential fraud, with someone pretending to be a user they’re not.
Unfortunately, AI cannot provide everything we want. When it comes to the cross-border and B2B space, AI is more limited in its uses. While businesses demand increasingly faster account opening and onboarding, the entirety of the process can’t be automated. The problem stems from a difficulty in standardising. Variations in geography, type of business, corporate structures, and even the individuals involved mean that a risk profile must be created for each case individually. Even if the processes could be automated to a higher degree, the risk to reward ratio may mean that the investment in AI isn’t sufficiently attractive. Simply put, financial institutions are rightly anxious about an automated system messing up in complex cases that could lead to massive fines or worse.
Moreover, there exists a question of accountability. When a decision is made by AI, how are you then able to find the exact reason behind why a transaction is not stopped when it should have been – other than to blame it on the algorithm? Using AI makes it very difficult to audit payments, as the fuzzy logic of Machine Learning is almost entirely obscure to us humans.
In short, yes, AI and automation are providing a much-needed breathing room for banks, financial institutions and fintechs looking to alleviate some of the AML burden. However, they are no panacea. Real-life, human bankers will stay with us for a while longer. And for those looking for banking with a friendly face, that may not be such a bad thing after all.
AI-Powered Fraud Prevention for Digital Transactions
By Martin Rehak, CEO of Resistant AI
Fraud is on the rise, thanks to the rapid escalation of digital channels in response to the unprecedented challenges created by COVID-19. However, this rapid shift to digital-first operations and transactions has come at a price for banks and financial services organisations. Which is why financial services organisations are increasingly turning to AI to intelligently address an ever-evolving and ever-smarter attack landscape.
If nothing else, COVID-19 helped shine a spotlight on the vulnerabilities of today’s digital and mobile customer platforms that are capable of executing rapid and instant payment transactions, leaving little time to undertake customer authentication or transaction verification. Similarly, the difficulties of Know Your Customer (KYC) and customer onboarding in the digital era is exposing financial services organisations – and the customers they serve – to a significantly increased risk of cyber-crime and financial fraud.
According to a recent UK Finance report, £754 million was stolen from bank customers in 2021 as scammers industrialised the use of authorised push payment fraud to trick individuals and businesses into sending money to bank accounts operated by criminals posing as genuine customers.
The challenge created by automation
The rapid expansion and automation of financial services to minimise friction for customers has created new challenges with regard to verification and risk management policies and practices. Evaluating if a digital interaction is authentic now depends on referencing a huge amount of data from multiple sources – everything from geolocation and session behaviours to data from merchants, bureaus, and customer profiles.
Added to which, today’s financial fraudsters are becoming expert at targeting these complex digital environments and are using innovations such as block chain and instant payments against banks and their customers.
Staying ahead of criminals is an imperative. Especially as directives like Open Banking open up third party access to customer data that further heightens the vulnerability of finance firms to fraudulent activities if this process is not appropriately monitored and managed.
Financial organisations spend vast amounts of money protecting their information and IT, yet the automated processes that deliver access to money are often the least protected. Traditional approaches to fraud prevention that rely primarily on human intervention have proved inadequate for preventing the activities of today’s sophisticated digital criminals, who are capable of exploiting vulnerable automated systems at scale.
In response, the finance sector needs to enable real-time identity forensics that brings together state-of-the-art document and customer behaviour evaluation to uncover synthetic identities, account takeover attempts, money laundering and other emerging types of fraud plaguing financial services.
Strengthening onboarding and KYC processes
Attaining a deep understanding of the end-to-end customer journey is now mission critical for combating fraud and financial crime. Onboarding and KYC represent key cornerstones in the mission to prevent scams. However, the shift to digital documents for ID authentication, combined with the relaxation of onboarding verification to expedite customer conversions during the crisis, have created significant opportunities for fraud.
In the onboarding process, identify validation is the first step to affirm an applicant actually exists. Next comes verification, which links that person to the information they provided in the validation stage. In many automated workflows there are risks from forged or manipulated documents that support the customer journey in online lending, trading, insurance, financing, factoring and payments.
Typically, 17% of bank statements used for lending applications or KYC purposes have been tampered with and 11% of UK payslips submitted as part of digital loan applications have been altered or are forged. Similarly, 15% of company registration certificates submitted worldwide when opening a bank account are fakes and 9% of utility bills submitted as proof of address are forged.
By protecting automated processes that use unauthorised documents from third parties, institutions can gain certainty that all digital documents are genuine. Similarly, continually assessing transactions will instantly alert teams to potentially fraudulent activities. These anomalies encompass behavioural, device characteristics, unusual switching between accounts and more.
Providing an intelligent shield for automated financial systems, AI powered fraud prevention delivers a convenient customer onboarding experience while limiting the generation of false alarms – ensuring that fraud and cyber analysts need only investigate genuine priority alerts.
Advanced fraud insights
Today’s AI-powered real-time identity forensics are capable of detecting advanced fraud and manipulation and are adept at joining the dots to uncover previously unidentified vulnerabilities and gaps in third-party systems, so that future potential exploitations can be deterred.
With financial criminals continuing to up their game, banks and finance organisations are leveraging AI technologies to strengthen the validation, verification and transactional processes that deliver enhanced security without compromising the customer journey or experience. With the right financial automation oversight technology in place, they’re better positioned to predict, detect and deter criminal adversaries and stay one step ahead of evolving new risks on the horizon.
SMART WEARABLES IN HEALTH TECHNOLOGY
Gavin Bashar, UK managing director at Tunstall Healthcare, discusses smart wearables in health and social care, the benefits, and what the future holds.
For many years, technology has been integrated into every sector in the economy, from banking to shopping, to enhance the experience of customers.
However, health and social care services have fallen behind in terms of technology adoption and innovation, for reasons including fragmented structures, limited resources, and reluctance to change.
Yet person-centred technology has the power to transform lives, not only enabling the ongoing delivery of support services to vulnerable people, but reshaping the health and social care sector as a whole.
Technology-enabled health and care is the service of the future and the ongoing and unprecedented rapid acceleration in the adoption of care and health technology has demonstrated the numerous benefits in practice.
Why wearable technology?
Wearable technology enriches the lives of a range of cohorts, including people living with long term conditions such as dementia, and connects vulnerable individuals to key stakeholders such as clinicians and family members.
The better application of technology and wearable devices can deliver significant benefits including improved patient outcomes and service-user experiences, a reduction in the strain on staff and carers, and potential cost savings or avoidance.
Wearable devices and the systems they’re linked to use wireless and digital technology to enable support services to be efficient, flexible, responsive, and tailored to the individual. The unobtrusive devices also ensure that care delivery is discreet and won’t interrupt the daily life of service users.
Proactive healthcare is also easier thanks to wearable technology. Service users become much more engaged with their own health and have greater opportunity to develop a proactive approach to their health monitoring, rather than reacting. Technology can be used to enable intervention at an early stage by identifying irregularities before they become more significant health or care issues which require expensive care and treatment.
There is significant evidence that wearable technology offers users greater choice in terms of the care they receive and prevents incidents in the first place, by recognising an emergency as soon as it occurs. Community alarms and telecare services in particular are effective methods of signposting to clinicians and additional services when a user requires care, and this has been particularly important during the pandemic.
Wearables in a home and residential care setting
When providers are presented with unique opportunities to drive the adoption of digital health solutions such as wearables, there must be a focus on designing holistic services which fit seamlessly into the user’s life, work with clinical practices, and ensure any data that is collected is stored securely.
There is a huge range of wearable technology and devices available which perform a number of functions and can therefore be tailored to suit the needs of an individual and their stakeholders, such as carers and clinicians.
Small, discreet pendants available on the market can raise alarm calls in emergencies, and protect users living independently at home or in group living environments. Features can include integrated alarm buttons, LEDs for visual reassurance that a button has been pressed, easy to wear options, and auto low battery monitoring and alerts.
Falls are the main reason that older people are taken to hospital and unaddressed fall hazards in the home are estimated to cost the NHS over £430 million1. Smart wearables use advanced technology to allow users to raise an alarm from anywhere in their home or care setting if they are in difficulty. Some devices can also automatically raise an alert if a fall is detected.
This technology offers confidence to individuals who are at risk of falling, such as people with limited mobility, the elderly, and people with long-term conditions such as epilepsy, diabetes and Parkinson’s disease.
Wearable technology not only benefits vulnerable individuals living at home, but also those in residential care settings and their carers. Nurse call systems which are integrated with smart wearables can be personalised to ensure individual safety with minimal disruption to other care home residents. It also respects dignity while improving management insights, workflow efficiencies, staff morale, and care quality.
Devices can also be worn which protect users when away from home, automatically detecting falls, offering an SOS function and providing the user’s location.
The benefits of managed technology and smart wearables
Technology can require equipment from a range of manufacturers. Identifying, purchasing and managing devices from multiple sources can prove challenging and resource intensive for local authority community alarm centres.
Nottinghamshire County Council (NCC) has a managed healthcare service which includes home units, telecare sensors and wearable devices which are all tailored to the needs of individual service users.
All connections are monitored and referrals are made to the NCC Responder team, nominated contacts or the emergency services, as appropriate. NCC also has Reablement Assessment flats with telecare in place to support people leaving hospital, helping them to increase wellbeing and regain skills to enable them to return home.
Between October 2019 and December 2020, significant benefits and improved outcomes have been observed. Over 280 cases where a high and immediate risk of admission to residential care were avoided, and over 650 cases which required additional community care costs were avoided.
In total, savings of over £2.2 million have been achieved after additional service costs, costs of homecare for people diverted from residential care, and loss of client contributions have been deducted.
The next generation of wearable technology
The deployment of smart technology, including wearable devices, enables vulnerable people to live safely and independently for as long as possible. However as demands change, the care journey is now evolving rapidly and healthcare services must adapt accordingly.
We’re beginning to see the next generation of predictive care technology and smart wearable devices, and over the next few years this will encompass integration that enables diverse and scalable models of health and social care. Using AI and taking data-driven insight from multiple sources, providers will use this next generation of solutions to optimise Population Health Management programmes by providing personalised and anticipatory care.
Smart wearables in health and social care are designed to improve quality of life and empower individuals to take control of their health, while supporting the NHS and additional stakeholders by reducing the number of required GP visits, ambulance callouts, hospital admissions, and demand for local authority funded residential care
For more information on how wearable technology can support the ongoing delivery of proactive and effective support, please visit www.tunstall.co.uk
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