IBSi Blogs

Many of us use physical biometric authentication every day when we log into our mobile devices. It relies on innate human characteristics such as fingerprints or iris patterns. But what are behavioural biometrics?

By Abdeslam Alaoui Smaili, CEO, HPS

The pandemic has accelerated the global journey towards cashlessness and digitalisation. The transition away from cash and towards digital payments has brought with it many benefits, including greater financial inclusion in developing countries, since those who were previously unbanked now have greater access to merchants and services through the use of mobile money.

With the emergence of real-time confirmation and settlement, merchants have greater visibility and view on liquidity. This greater transparency is also helping governments to develop better-regulated tax systems and to more easily identify fraud and financial crime.

In order to combat the heightened risk of fraud that comes with the increased use of digital payments, it is vital to adopt rigorous digital authentication and security measures to protect consumers – and biometric measures can help to bridge this gap.

It is estimated that nearly 90% of smartphones around the world will have a form of biometric capability by 2024, according to research by Juniper. It also forecasts that $2.5 trillion in mobile payments will be facilitated by biometric data by 2024.

But what are behavioural biometrics?

In the payments world, behavioural biometrics, also called DNA mapping, are used to prove the identity of the user, authenticate the user and prevent fraud. For instance, mobile and online experiences built with behavioural DNA mapping can ensure a seamless and secure customer experience by analysing multiple data sets including the way a user holds their phone (in their left hand or right hand), the sizing of their hand, the way they swipe, navigate, or even the way they turn on the mobile.

Today’s behavioural biometric platforms can collect more than 2,000 parameters from a mobile device by leveraging artificial intelligence (AI) and machine learning (ML) techniques. The collected data is used to create and train a customised security model for each user in order to secure his account and differentiate him from impersonators and robots. The trained models are polled to give an optimal prediction in real-time while the user is logging in, and as result the fraud detection can be accomplished without impacting the login performance.

The perfect match for payments

Since behavioural biometrics offer a frictionless authentication method, it is ideal for digital transactions. It does not exert any change into the user experience which keeps the effort required on the part of the consumer to a minimum.

Behavioural biometrics have strong fraud detection capabilities: it is possible to distinguish a real user from an impostor by recognising normal user behaviour and fraudulent behaviour in real time. For example, if you somebody was to steal your phone and try to log into your phone, your mobile wallet or your online banking applications, an efficient behavioural biometrics solution will be able to block the user, even if the password used is the correct one – simply because the way the thief used your phone would be different to the way that you use it.

It can also be used to detect fraudulent activity online and to help distinguish a robot to a human user by analysing several elements. For example, how is the text being typed? How long does it take the user to fill in each field? How does the user navigate the website? Do they usually scroll this fast? Are they taking longer than usual to answer their memorable information?

Behavioural biometrics continues to strike the right balance demanded by the payments landscape. The authentication is invisible, but mobile and online payments remain secured. As more businesses, governments and countries begin to digitalise in response to the Covid-19 crisis, the demand for behavioural biometrics technology and its ability to protect consumers looks set to grow.

2020 has been a year of challenging moments in wealth management. From a pandemic to a recession, and Brexit. For investors, these challenging moments are represented in asset price volatility, ultra-low interest rates and an uncertain financial market.

By Christophe Lapaire, Head Advanced Tax Services,  Swiss Stock Exchange

As we approach and sail past these various milestones, how are investors expected to fare over the next couple of years? Although markets seem to be coming back on track, both private banks and wealth managers globally still have big questions around what they can do to ensure performance in investment portfolios.

With that in mind, many private banks and wealth managers are starting to carry out the act of utilising tax optimisation. Helping them to stay prepared for any more upcoming uncertainty.

The impact of unexpected changes in our ecosystem on tax optimisation

Many private banks, investor clients or wealth managers may be aware of the benefits that it brings, however, not all have the capability to utilise it. Outdated technologies and manual processing within their infrastructure are not ideal for delivering tax optimisation services.

That said, due to regulatory changes that have cropped up around unexpected situations such as the pandemic and the following recession, some businesses have been pushed to update their technologies, now putting them in a better position to deal with current and potential uncertainties.

The private banks and wealth management firms that have been utilising tax optimisation view it as integral to the overall investment offered as it helps to reduce tax charges to a minimum by using the advantages of the law, without violating tax laws, whilst reclaiming all foreign withholding taxes.

Effective tax optimisation

Although tax optimisation benefits all, it is not necessary for every country as many provide capital gains exemptions. However, the tax performance of those countries that do not, will suffer, impacting any and all portfolio’s that are not optimising effectively.

Effective tax optimisation is essential if you want to manage and reinvest funds easily, whilst not being impacted by the worst of any tax leakage. Taxation requirements are not always uniform within countries and this lack of expertise can also lead to incurring tax that should have been avoided or mitigated.

Tax optimisation should be seen as integral and those who do not jump on-board sooner rather than later risk falling behind to the private banks and private managers that do.

There’s no ‘one size fits all’ answer

Nonetheless, tax optimisation is not always the answer for every private bank or wealth management firm. They all have different systems and infrastructures and there is no ‘one size fits all’. Without those up to date systems in place, some of these processes would have to be done manually, and this can end up being incredibly labour intensive.

Wealth management providers that offer tax optimisation services must make sure that they have the appropriate setup to report their clients’ tax information. Fortunately, it’s not the end of the road for those who don’t have the right software for maximum efficiency as they still have the option of using tax reclaim services – such as the Swiss Stock Exchange’s advanced tax service. This will help them to be sustainable and create savings without increasing labour levels, generally at an affordable cost that brings value to their clients.

Within our current climate, investors are continuously seeking out innovative solutions for tax optimisation and the private banks and wealth management firms that have the capability to offer it will be the ones that investors go to. Unfortunately for the providers without these services, the risk of falling behind and losing clients to companies that do provide them is great.

By Guy Warren, CEO, ITRS Group

On February 20th 2020, the markets began to react to Covid-19, as one country after another was plunged into lockdown. On the 11th March, the World Health Organization declared the outbreak a pandemic and by March 23rd the S&P had lost 34 per cent of its value.

Fast forward twelve months and the trading landscape has changed forever – but not necessarily as a consequence of the virus. Instead, the retail trading revolt of Reddit users has been the true catalyst to change the game entirely. Even more so than a global pandemic.

Until now, retail trading has tended to shadow the market, rather than move it significantly one way or another. That luxury was previously reserved for institutional investors or hedge funds. Yet, following the successful coordination of a large group of traders the power dynamic has shifted, and the ability to move the market is now accessible to all. And although the democratisation of investments is welcomed, the activity exposed the vulnerability of global market infrastructure, while also exposing the weaknesses of individual firms trading systems. Reddit users placed significant stress on trading structures as volumes surged, resulting in multiple outages across high profile retail portals. Systems should be ready for anything, yet just short of a year since the markets collapsed, those who have been preparing for the unexpected are still being floored by the unimaginable.

Over the next twelve months, the power of the retail investor will grow. Lockdown has left people with more money and fewer places to spend it – alongside a growing awareness of investing. And while experts are still unsure of exactly how the market will react to this new phenomenon, firms must get a handle on the exact volume their systems can take.

To begin future-proofing themselves, firms must first understand their present headroom. All systems have a limit of how many trades they can do per minute, yet many firms do not know what the limit is, let alone how to address potential points of failure. Now is the time to end the trial-and-error approach to capacity and get a handle on the exact volume their systems can house today.

Capacity planning tools are essential, helping firms to not just calculate their headroom, but identify where potential pinch points exist within their IT systems. Modelling and stress testing also play a crucial role in the capacity planning of systems. The right software tool allows you to stress test ‘worst-case’ scenarios, which then enables firms to put in place plans to deal with this. By using machine learning and modelling scenarios that haven’t happened yet, firms can better predict what their systems can and cannot withstand. Companies need to avoid taking a stab in the dark regarding how much capacity their system can hold. They can use predictive scenario models such as ITRS’ ‘forward-thinking’ solution to model a variety of worst-case scenarios.

Uncertainty is the order of the day, whether you’re an individual or a business. Yet, if the last twelve months has taught financial services anything, it’s to be prepared for the unexpected. By utilising the right IT software, firms can gain vital insight into their IT estates and prepare themselves for the unimaginable.

Guy Warren
CEO
ITRS Group

Credit risk and fraud risk are often discussed in relation to one another but in truth, determining an individual’s fraud risk is not the same as determining their credit risk. An evolving fraud landscape with increasingly sophisticated methods requires new tactics for mitigating fraud risk. This means moving away from the old, rigid credit risk assessment tactics.

By Beth Shulkin, VP Global Marketing, Ekata

In the 1980s and early 1990s, the traditional method for determining credit risk was based on data tied to consumer credit histories, and only done for mature credit markets. This information was used by the government to identify the correct person for payments, such as welfare, social benefits, wages, and stimulus checks. Banks and other financial institutions also leveraged this data to process account openings and assess loan worthiness. Credit data was essential for preventing mispayments, flagging individuals who do not pay back their loans, and more.

When the tech boom occurred in the mid-1990s and e-commerce began to take off (as well as digital fraud), companies turned to a method they were already using to determine credit risk and prevent fraud – using namely credit data. By utilising easily accessible information like addresses and ZIP (post) codes, the companies could determine if an individual making the purchase was real. However, the massive number of security breaches that occurred in the 2000s, including Equifax in 2017, compromised much of this credit data. Non-fraudulent customers trying to make valid purchases were often flagged as risky, even if they were perfectly legitimate customers, leaving money on the table for businesses and creating unnecessary friction for buyers. According to Gartner there is a greater than 50% chance that an individual’s credit data is already in the hands of a cybercriminal. With this in mind, businesses are finding new ways to determine creditworthiness.

Fraud Assessment to Determine Risk

Modern businesses are leaving behind old, rigid credit risk assessments, and are turning their attention to new approaches for determining the probability of fraud risk. This assessment leverages new types of dynamic personally identifiable information (PII) to make a risk assessment, and new technologies (such as machine learning) to help organisations anticipate the behavior of potential fraudsters.

There are three ways this type of analysis is helpful for businesses:

1. It eliminates friction in the digital customer journey: Credit risk makes a determination based on a set threshold. For instance, customers must meet a certain credit score in order to be eligible. Fraud risk looks at the likelihood that a bad actor is behind the digital interaction. Using a probabilistic approach to risk assessment for digital fraud can help businesses move away from utilising rigid, friction-filled deterministic methods to fight digital fraud. This creates a smoother process for good customers while also flagging suspicious online activity and protecting the business.
2. It provides a more comprehensive assessment: The PII used for credit risk analysis is based on static information (social security numbers, government IDs, phone numbers, etc.) most of which has been compromised. While the information used in probabilistic fraud risk analysis utilises dynamic PII and more importantly the links between those attributes and how they behave online. Dynamic PII moves beyond credit history determinations and instead looks at device ID, IP, emails, consumer behavior, metadata, and biometrics, to get a better sense of the customer risk. By evaluating the multiple dynamic linkages between these elements, organisations can learn how consumers are behaving online and provide a more comprehensive assessment of risk in fractions of a second.
3. Extends beyond border limitations: Another issue with using only a deterministic approach with credit data is that it resides in country-based silos in only around 20 mature credit markets, making it difficult for businesses to evaluate risk internationally or across borders. Dynamic PII elements can circumvent this issue and be leveraged with a consistent data format around the world to assess risk.

 

A rigid, deterministic approach was useful for fraud detection when e-commerce was in its infancy, but in today’s world, it simply isn’t sustainable. More than 70% of consumers say account creation should be instantaneous. An overwhelming majority also expect a fast, frictionless experience while also getting one that is as trustworthy and secure as possible. As data breaches continue to compromise customer’s credit information, it’s imperative that organisations move beyond traditional risk analysis and shift toward new ways to protect themselves and their customers. Dynamic PII used through machine learning is the future of fraud analysis, and by utilising a wider breadth of data, businesses can enable a quick and easy process for their good customers while mitigating risk.

Achieving excellence in omnichannel customer experience is both imperative and a huge source of competitive advantage in the world of digital banking. Here are four fronts that banks need to act on simultaneously to achieve a successful digital-first omnichannel banking.

By Puneet Chhahira, Global Head, Marketing & FinTech Engagements, Infosys Finacle

Digital is disruptive, pervasive, and transformative. The ubiquitous digitization of our world that has upended businesses and organizations across all industries has had a transformative impact on the financial services industry – reshaping the whole customer-experience ecosystem and legacy business models.

The crisis ushered in by the pandemic has further heightened the level of urgency for digital transformation, proving to be one of the positive outcomes of the pandemic. That said, banks are still moving at a slower pace than desirable. This is corroborated by the findings of the Infosys Finacle Efma ‘Innovation in Retail Banking’ 2020 report¹ – only 7 percent of the 700 banking executives interviewed believe that their organization has deployed digital transformation at scale and is reaping the desired results. The remaining 93% are at different stages, with the highest being 49%, confirming that the digital transformation is partially deployed and is delivering as expected.

Constantly evolving, omnichannel banking in the digital age means that banking must be accessible on all the channels of discovery and value delivery, including mobile, internet, chat, voice banking, and smartwatch. The next step is to embed financial services so deeply within customers’ lifestyles that they are virtually invisible; examples include integrating peer-to-peer payments within social channels, consumer loans within e-commerce sites, or “buy now pay later” features. A roadmap for getting there could possibly look like this:

1. Reimagining the business model:

The vertically integrated pipeline business model in financial services – bank manufacturing its products, matchmaking products with its customers, and distributing through its channels – is breaking apart and giving way to distributed platform-business models. There is tremendous evolution happening across this linear value chain. Let us look at each of these layers individually.

Today, some of the most progressive banks globally are platform businesses that aggregate a wide range of financial and non-financial products from various providers. They are transforming their product portfolios by:

• Creating game-changing joint products with other banks/FinTechs/digital giants – Apple partnered with Marcus by Goldman Sachs (and Mastercard) to launch Apple Card
• Embedding non-financial lifestyle products into their journey such as hotel, flight, cab, event bookings, movie ticketing, among others
• Collaborating with third parties in delivering competing products such as higher interest-paying deposits or a unique lending proposition- Paytm has joined forces with IndusInd Bank offering high value fixed deposits and with ICICI Bank to offer digital loans.

On the channels’ front, banks look to offer aggregated products and services not just through their own channels but also through API-led distribution on third-party channels, apps, non-bank channels such as smart home automation devices.

Given the fragmentation happening across these layers, a bank can choose to focus on a platform-business model in one of the three ways:

1. Be a banking manufacturer that makes best-in-class products that it sells through various self-owned and third-party channels. For example, bank leveraging third party channels to sell their credit products
2. Be a banking marketplace that offers a combination of self and third-party products. For example, Starling bank from the UK offers a marketplace providing services from best-of-breed partners in the area of accounting software, wealth management services, pension accounts, among others.
3. Offer banking on a platform by providing products and services to Neo-banks to set up new businesses. For example, Telefonica Deutschland, a mobile telecommunications company, launched O2 banking – a mobile-only bank account built on German bank Fidor’s platform. It enables transactions through mobile, offers small instant loans and better mobile data plans.

 

2. Reimagine customer experience for the open banking world

Customers today are spoilt for choice. They are highly demanding, impatient, and would not hesitate to switch from their preferred brand after just one bad experience. The rapidly unfolding digital trends have further pushed the envelope on customer engagement: in the past 20 years, banking transactions have gone from 50 percent in-branch to 95 percent digital self-service channels. Customers are unwaveringly shifting to platforms owned third-party channels of the open economy. In India, for instance, over 85% of the open payments transactions (UPI-based payments) are recorded by non-banking players like Google Pay, Phone Pe, and Paytm.

Another emerging trend is embedding banking into the primary journeys of the customer. For example, a car financing journey will commence not when the customer needs a loan but when the customer is considering buying or upgrading a car. For instance, DBS participates in the customers’ primary journey by operating successful marketplaces for used cars, property, travel, and utilities. This also extends to business banking, where leading banks are integrating their services through popular ERP solutions.

Finally, on the roadmap to customer-centricity, leveraging modern technologies such as AI, mobile, open-APIs, augmented and virtual reality will play a determining role in delivering experiences that are a lot more personalized, contextual, and outcome-oriented that customers will prefer.

 

3. Turning Data into Competitive Advantage

Data is the key. It is driving the success of both Big Tech and FinTechs in spaces traditionally occupied by banks. For example, Google’s foray into autonomous cars is driven by their success with maps. Banks need to move from traditional interest and fee income models to data-led monetization models – lest other digital platforms do the same and eat the market share. They must look beyond segment-based offerings and pricing to customer-specific offerings and pricing. For instance, loans can move from uniform lending rates to individual pricing.

They must leverage the power of big data and advanced analytics to anticipate customer behavior and requirements and use these insights and other data, such as location and payment preferences, to push contextual, personalized offers at scale.

 

4. Drive ubiquitous automation to reset the industry benchmarks

Automation is a critical competitive strength. Digitisation has radically altered the cost-efficiency in banking. Simply compare the cost-income ratios of the top 1,000 banks, 50 percent on average, with the 40 percent of digitally advanced banks and 30 percent of digital-only banks to understand the cost pressures the incumbents are facing. In a world where digitization has become the default, incumbent financial institutions would thus need to double down on their automation journeys to reset the benchmark – operate at a higher level of efficiency, increase the ability to price well, and ability to drive sustenance.

Technologies such as RPA, cognitive automation, API, blockchain, cloud, etc., will help drive automation and operate at a much efficient level. With enhanced cognitive technologies, banks will be able to progress into an environment where processes with machines and software at either end would bring up the possibility of autonomous banking. Customer service will almost entirely move towards self-service channels supported by smart assistants, where required, or we will witness an era of near-zero back-office where smart machines manage the entire processes. Think of automated banking tasks driven by google assistant. Or self-driving cars paying for fuel themselves. This will enable the delivery of smarter services.

 

Endnote:

Achieving excellence in omnichannel customer experience is both imperative and a huge source of competitive advantage in the world of digital banking. Banks need to act on all four fronts in parallel to achieve a successful digital-first omnichannel banking.

Sources:

1. EFMA, Infosys Finacle: Innovation in Retail Banking 2020 – https://www.edgeverve.com/finacle/efma-innovation-in-retail-banking/

Fixed income products are seeing a surge of interest due to uncertainty in other financial markets and in response to funding programmes set up by governments to mitigate problems created by the global health crisis.

By Rowland Park, CEO & co-Founder, and Simon Gregory, CTO and co-Founder, Limeglass

The scenario of government bonds with negative yields and huge bond-buying programmes by central banks to alleviate the economic stresses caused by the Covid-19 pandemic was not a likely situation a few months ago. Yet, financial authorities are now devising a raft of measures to help global economies remain liquid and businesses to remain operational.

In such volatile markets, the need for banks and investors to access appropriate information to generate a positive outcome is heightened. Everything from the latest news on a Covid-19 vaccine to the continued trade tensions between China and the US are impacting markets. Consequently, the ability to find and assimilate information on a broad range of topics is hugely valuable. For example, clients may want to understand both the impact of coronavirus on a specific country and that country’s new emergency monetary policies.

Yet is it always possible to quickly identify this information in your body of research?

The quality of content that financial researchers produce is incredibly high and the value of their insights to traders is significant. However, with budgets under significant pressure and research costs being separated from trading, report providers are having to work harder and smarter to demonstrate their value.

One of the key challenges is that research users often have difficulty locating all the relevant insights within the huge quantities of reports produced. This is the usual problem of information overload that every market participant suffers from. In the fixed income markets, where bonds are affected by a wide range of macro and micro factors, the problem is particularly acute. There are no easy ‘tickers’ for companies to identify these factors.

Banks produce and receive thousands of pages of research each day on everything from the global economy to political statements and share prices. This overwhelming mass of documentation can mean that key information and specific insights are not spotted.

Traditional methods of managing the influx of research, such as scrolling through an email inbox or using the ‘Control+F’ search function, are slow and only provide results that match exactly to the search term. Anything using a synonym or related phrase will be missed entirely.

This ineffective use of research represents a systematic loss of value for investors. To remedy this, research producers must maximise their output by enabling their clients to access specific, relevant details quickly. By applying technology to research reports, producers can provide a far more personalised, effective and valuable product.

Document atomisation

Fixed income participants need pertinent information at the right time to make the best decisions. So how can firms ensure that they provide their clients with only the relevant research while nullifying the prospect of fundamental information being hidden?

The key is ‘document atomisation’. With Limeglass’s technology, this means breaking down reports into paragraphs, understanding the topics they contain, tagging them with synonymous and relevant ‘smart’ tags, and mapping these within the system to provide a correlated directory for the fixed income market.

By approaching documents in this way, focused on concepts rather than specific words, document atomisation ensures that a search is not restricted simply to verbatim language results. The trick here is to understand the context of each paragraph. It is the combination of these granular smart tags that allow participants to select individual paragraphs from hundreds of documents at the click of a mouse.

As an example, let us consider what you might search for if you’re thinking of buying bonds in an emerging market economy such as Malaysia. The impact of both Covid-19 and the country’s monetary policy response would be factors. You may want to know more about the tapering of the MCO (Movement Control Order) while also looking at the success of stimulus packages such as PRIHATIN. It is unlikely that you would be aware of all the country’s financial programmes, but a simple search for ‘Malaysia COVID-19’ and ‘Malaysia Monetary Policy’ will surface all the relevant paragraphs from a multitude of documents, presented to you in one view. In providing synonymously tagged results from multiple sources, in an easy-to-access format, the context allows users to easily analyse what is relevant for their requirements.

In this way, the atomisation and tagging processes turn unstructured reports into usefully structured material, giving a comprehensive overview of fixed income for the client.

Rich Natural Language Processing (NLP) is an integral part of automating this process. Applying this linguistic branch of artificial intelligence is intrinsic in identifying the actual context of the paragraph.

Knowing all the themes, as well as having granular metrics on every topic being written opens up all sorts of interesting opportunities for maximising current research.

This technology, along with human guidance, means that new additional phrases are continually added, and ensures that a level of contextual awareness can be applied to the atomisation process. Prior to the advent of NLP technology, such tagging would have been an arduous and time-consuming manual process.

How does this help fixed income markets?

Such a methodology not only offers a relevant, detailed and convenient manner of consuming reports, but also means that the results are – by their nature – personalised to the user. In today’s complex financial industry, a one-size-fits-all approach to research cannot provide the level of relevance and detail which market participants require. With increasing capabilities for using technology, a lack of personalised output is a loss of opportunity.

A firm may know what areas of fixed income their clients are interested in, but if there is no ability to only surface or distribute the precise topics the readers are interested in, the material will be of limited value and may not be read or fully appreciated. In disseminating specific paragraphs, the time and cost savings bring extensive benefits to both the firm and its clients.

With this technology, the recipient can assess the relevance of any fixed income reports much more quickly, and in so doing, the consequence is an enhanced relationship between the research producer and the client.

A personalised flow of information will lead to better informed fixed income trading decisions. Moreover, the process provides a competitive edge for research firms and thereby leads to business success.