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AI-Powered Fraud Prevention for Digital Transactions

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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.

 

Business

How Big Data is Transforming Bilateral Trading

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By Stuart Smith, Co-Head Business Development – Data & Risk

 

Since its inception, Big Data has been an important part of how firms have identified and constructed quantitative trading strategies with hedge funds depending more on quant strategies which rely heavily on big data driven analytics.

As big data technology continues to move from being a specialised technical capability to being a commoditised capability available on a range of easily consumed technology platforms, its use within the financial derivatives will continue to increase beyond the initial quantitative driven capabilities.

At the same time, the number and range of available data sources is increasing rapidly. Whether it’s the increase in alternative data sets or new technology enabling firms to simply keep more of the data they have been creating, the volume of data available is increasing dramatically.

 

Big Data in Risk Management

Risk Management has always had requirements which have driven a close collaboration between business and technology to make available risk analytics useful for the business to make better decisions. As technology becomes more advanced, the metrics available continue to improve as well. This is typically because many risk metrics require high numbers of scenarios and valuations to correctly identify risks in multiple scenarios. To maintain flexibility, this has led to an explosion of data to manage. Firms are increasingly keeping all this data available which can run into many Terabytes (TBs), much of which needs to be ‘In Memory’ to make it accessible to analysts.

Stuart Smith

To achieve this big-data, technology is critical to allow firms to move large volumes of data quickly and easily from affordable long-term storage into high performance in-memory analytics. Big Data technology is ideal for this type of problem to enable large volumes of data to be recalled from across multiple stores and appropriately aggregated or filtered based on the analysis which users are requesting. Whereas in the past, analysts would have to accept that data outside of the last 3-5 days is only available in a summarised format, they can now expect that the data can be re-hydrated quickly and easily from cloud data stores and available to them in an easy-to-consume web interface.

This can enable much more dynamic types of analysis, for example where a new risk is identified, through analysis of a recent data set it’s now possible to find a long history of that risk, whereas previously it would have been lost through summarisation and fixed reporting processes.

 

Collaborative Data Sets

More big data stores are being created as the industry becomes more collaborative and uses increasing numbers of fintech solutions and platforms. With this change come new ways to analyse data and provide new insights.

For instance, through the automation of collateral exchange, an historical store of margin calls, payments and disputes has been created. This history provides a resource for banks to understand their performance in accurately issuing and making margin calls based on derivatives and compare their performance to that of the industry as a whole. The example below shows how a firm can be benchmarked while holding other institutions data private.

These types of analysis are new and could not be delivered without the centralised collaborative data model. It can prove to be instrumental in improving firms’ overall operational efficiency and client service.

It also provides an opportunity for Machine Learning techniques, based on big data sets, to analyse and predict payments requests which are likely to be disputed and potentially identify causes before an actual dispute is even raised. This type of ‘self-healing’ process can only be enabled by a large history of data through which algorithms can be trained.

In the case of Initial Margin (IM) calculated by ISDA SIMM* a new set of challenges have been introduced through having a two-sided risk calculation as part of the process of deriving payment information. This adds another level of complexity to the resolving of disputes; however, the potential offered by having large volumes of data opens up new options on how this challenge could be solved. The long history of Common Risk Interchange Format (CRIF)** data provides a long-term view of the sensitivities for most OTC derivatives, which can enable firms to identify basic issues like stale market data day over day. However, as with most detailed analysis differences in models, they can also be identified through looking at differences over long periods of time. Identification of these types of model discrepancies can help firms to be more proactive about reviewing their modelling deficiencies to ensure that differences don’t lead to disputes.

 

Looking ahead

The sheer volume of data can be an industry-wide challenge with firms having to manage disparate, needlessly duplicated and ultimately overwhelming information. Creation of an industry standard for reporting and analytics is, therefore, crucial to enable firms get clarity and valuable insights from the masses of data and centralise the information as a single data layer. Acadia has designed Data Exploration (DX) suite to be one-of-its-kind big data analytics platform to help sell-side, buy-side and fund administrators see its market positioning, trends and analysis of industrywide metrics.

The impact of big data will only grow and the industry is left with no choice than to evolve the use of technology, whether that is to drive quant strategies for hedge funds, more dynamic forms of risk management or larger shared industry data sets. All of these applications rely on underlying big data technology platforms to provide distributed analysis capabilities. As these capabilities continue to develop so will the types of analysis which are available to firms.

*The ISDA Standard Initial Margin Model (ISDA SIMM™) is a common methodology for calculating initial margin for non-centrally cleared derivatives, developed as part of ISDA’s Working Group on Margin Requirements (WGMR) to help market participants meet the BCBS-IOSCO margin framework for non-cleared derivatives.

** The CRIF file (Common Risk Interchange Format) is the industry template used to hold and exchange sensitivity data. ISDA’s calculation specifications are used to produce Delta, Vega and Curvature sensitivity numbers at Risk Factor-level

 

 

 

 

 

 

 

 

 

 

 

 

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Banking

How Biometric Payments Are Tackling Financial Exclusion

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By Catharina Eklof, CCO, IDEX Biometrics

We are moving closer to a cashless society: 89% of payments in the UK are contactless and, globally, contactless payment transaction values are set to surpass $10 trillion by 2027. Ease, convenience, security, and inclusion have accelerated the transition away from cash. However, many of today’s current payment solutions are leaving entire cross sections of society behind: including the most vulnerable, underserved, and unbanked populations.

Developments in the payment sector over the past decade still aren’t a perfect fit for all. Those suffering from dementia, literacy challenges, or impaired vision can find current payment methods – with a PIN to remember – extremely challenging. Financial inclusion requires us to make payments accessible to all demographics. Though the financially excluded represent minorities, they account for an estimated 1.7 billion people – almost a third of adults globally.

Enabled by huge advances in technology, our evolving social dialogue has become accelerated and unfettered, on a global scale. It is critical to harness technology as a force for dynamic economic improvement: democratizing access to banking and payments. As such, we need to look beyond mobile wallets or digital payments and support those in need of easier access to payment and fintech solutions. A more inclusive form of payment technology is essential.

Catharina Eklof

 

Personal Identity as the New Pin Code

Many communities remain vulnerable or underserved by the functionality of traditional payment solutions such as bank cards. These products are, at their core, only linked to the owner by way of name and signature, offering limited security and protection. With contactless payments, no link whatsoever is required to a card for payment.

In an increasingly contactless society, fraud and digital security are growing concerns. Credit and debit cards can be used by anyone, and card readers don’t understand if cards have been apprehended illegally. Vulnerable groups may also struggle to input their credentials into what can be, for some, a complex system. Empowering those vulnerable groups therefore means providing them with the independence to access payments with greater ease.

Biometric payment cards play a significant role in bridging the gap between the financially underserved and the financially included. Simple and secure financial authentication, like facial or fingerprint recognition, allow payments to become about who a person is rather than what they know or remember. If individuals can be personally linked to a payment card via biometrics, it can address the significant 1.1 billion people worldwide who are currently without official government identification or access to it. In Nigeria alone, 149 million individuals lack the legal means to evidence their identity, while in South Africa, 12 million individuals are excluded from the country’s formal identity system.

Fingerprint authentication has the added benefit of optimizing security, in that it requires the individual to opt into a purchase, avoiding any issues of unauthorized or unintentional payments from having a reader placed near the card owner’s face. This provides increased independence for the blind and visually impaired, who account for an estimated 2.2 billion people globally, as it allows for seamless payment authentication without sensory barriers. Similarly, biometric smart cards can be transformative for more than 55 million people living with dementia and Alzheimer’s, as it enables access to payment without the difficulty of remembering passcodes.

Literacy is also a little talked about hurdle to inclusion. Globally, there are 750 million “functionally illiterate” individuals struggling to use and understand financial products. Across all levels of education, biometric authentication is a universally inclusive concept. It is easy to communicate and understand that one’s fingerprint is inherent to their identity, and can act as a form of verification. Biometric smart cards facilitate and secure payments with ease by simply requiring their fingerprint to instantly authenticate their own card.

 

Pushing on With Progress

Even the most reluctant individuals are likely to have succumbed to contactless payments and some form of digitized banking in recent times. This will have the positive impact of making the needed transition to biometrics more seamless. Using fingerprints or facial recognition to unlock phones or access apps is not unusual. If anything, they have been convenient and comforting additions to the surge of tech innovations over the last couple of decades. There is a relief in knowing that these portals are being secured by methods that are almost impossible to replicate.

It is a breakthrough that financial players and governments in the world’s most developed countries still need to catch up with, as emerging economies have already capitalized on biometrics’ capabilities for almost a decade now. In India, for example, internal fraud and leakage from pension payments dropped by 47 percent after transitioning from cash to biometric smart cards. Because the solution bypasses the need for prior credit ratings or credentials, the country has also been able to catalyze safe online banking among previously unbanked adults since biometrics’ introduction in 2014.

Meanwhile, in Pakistan, the total number of mobile wallet accounts tripled from 5 to 15 million in 2015, with an estimated 50 percent of new registered mobile wallet accounts opened using biometric authentication. This was a result of Pakistan’s National Database and Registration Authority’s (NADRA’s) effort of collecting biometric information to allow for more convenient and democratic account opening processes.

Many around the world have been marginalized by both the pace of change in banking and the solutions that have, to this point, been created to accommodate such change. With the mass adoption of biometric smart cards, the same benefits seen in India could be realized on a global scale. If we take on the opportunity in front of us – promoting solutions like biometric smart cards to increase accessibility to the global economy – we will foster a digitally-focused, equitable and inclusive society. This doesn’t just mean ease and convenience, but also security for all and financial inclusion of those who have been left out of digital evolution, until now.

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