IBSi Blogs

The last 10 years have seen significant changes in the debate around what the most important channel for business is – brick and mortar or digital channels. But in the shadow of the pandemic, and the accelerated shift towards digital it spurred across the world, that debate has been well and truly put to rest. We’re now in the digital-first era, and this has meant financial organisations such as banks have needed to significantly transform business models.

by Amir Nooriala, Chief Commercial Officer, Callsign

Banks have traditionally relied on branches to drive loyalty and deliver experiences to customers. But with digital channels such as websites and mobile apps now becoming the most important avenues for business, old methods of driving experience and loyalty have been replaced by digital factors, such as app ratings.

So, it’s no surprise that in a digital-first world, businesses must leverage digital-first solutions. But if satisfaction levels are judged by the quality of a digital experience, then businesses not only need to ensure smooth and seamless access to services but also make sure security is a top priority.

However, analogue solutions that many organisations in finance still use for customer authentication – such as one-time passwords (OTPs) – fall short of achieving these goals.

The pressure is on for businesses to find new, truly digital-first solutions that make their customers’ lives easier and differentiate their experience from competitors. But this is a balancing act that needs to also ensure the safety and security of every online interaction because if ease comes at a cost to security, businesses risk losing customers altogether.

Vulnerable systems built on outdated foundations

When it comes to financial institutions, in particular, the ability to adapt to the times has always been part of their success. Some banks are centuries old, and to remain relevant they’ve had to adapt their business models, services, and cultures countless times, all while maintaining the integrity of the sensitive information they hold.

However, over time, that imperative to maintain the safety of their data has led to the accrual of legacy systems for most organisations. While this has been a long-standing problem, in a digital-first world, legacy systems present a particularly glaring vulnerability when it comes to authentication.

Throughout the pandemic, hackers and ransomware attackers took advantage of global uncertainty and thousands of people faced a barrage of text message-based scams over this period. This highlighted the already significant problem with commonly used authentication methods that so many businesses rely on.

In fact, almost a quarter of people questioned for a recent Callsign survey said they received more texts from scammers than their own friends and family.

Organisations’ unwillingness or inability to stop utilising outdated authentication processes such as OTPs are fuelling a worsening crisis in scams and fraud. Businesses developing their digital transformation strategy need to see it as a chance to approach everything they do with a digital-first mindset, and not simply try and recreate digital versions of existing solutions.

And to make this a reality, organisations must rethink their systems in line with how their customers actually behave online and build their solutions accordingly.

The verified path to digital-first innovation

There are a number of technologies that are ideal for financial organisations looking to elevate their authentication methods to digital-first levels that seamlessly and frictionlessly integrate into their customer journeys.

One such solution is passive behavioural biometrics, software capable of taking into consideration millions of data points when verifying the identity of a user. The key difference between behavioural and physical biometrics – and why behavioural biometrics is the superior authentication method – is that it isn’t reliant on a single device (it’s device agnostic).

So, when combined with device and threat intelligence, the solution can circumvent the single point of failure issue that so many other authentication methods fall foul of.

This makes it ideal for our modern, digital-first world where customers want to use a variety of devices and channels to access online services. By making your authentication method device-agnostic, user experience can be ensured in a secure and non-disruptive way, whatever device is being used.

And as this solution can be seamlessly integrated into any point of the customer’s journey, it’s a much more fitting solution for a sector that has always been on the cusp of innovation.

Re-building for a new era of authentication

In order to thrive, organisations in the financial sector need to be prepared to interrogate every aspect of their operations to make sure they are delivering the most convenient and secure service to customers online.

To achieve this, organisations need to be prepared to re-lay their technological foundations. Elevating the importance of digital identity authentication is one such vital change, and businesses need to realise it will only continue to grow as a priority for customers going into the future.

Because the threat from bad actors and cyberattacks is only worsening. So, while innovation is key to attracting customers, security must be at the core if businesses are to retain loyalty. And it will take collaboration both internally and with partners to ensure that customers have the security they deserve.

Financial services firms are seeing rapid growth in data volumes and diversity. Various trends are contributing to this growth of available data across the sector. One of the drivers is that firms need to disclose more to comply with the continuing push towards regulatory transparency.

by Neil Sandle, Head of Product Management, Alveo

In addition, a lot more data is being generated and collected through digitalisation, as a by-product of business activities, often referred to as ‘digital exhaust,’ and through the use of innovative new techniques such as natural language processing (NLP), to gauge market sentiment. These new data sets are used for a range of reasons by finance firms, from regulatory compliance to enhanced insight into potential investments.

The availability of this data and the potential it provides, along with increasingly data-intensive jobs and reporting requirements, means financial firms need to improve their market data access and analytics capabilities.

However, making good use of this data is complex. In order to prevent being inundated, firms need to develop a shopping list of companies or financial products they want to get the data from. They then need to decide what information to collect. Once sourced, they need to expose what data sets are available and highlight to business users, what the sources are, when data was requested, what came back, and what quality checks were taken.

Basically, firms need to be transparent about what is available within the company and what its provenance has been. They also need to know all the contextual information, such as was the data disclosed directly, is it expert opinion or just sentiment from the Internet and who has permission to use it?

With all this information available it becomes much easier for financial firms to decide what data they wish to use.

There are certain key processes data needs to go through before it can be fully trusted. If the data is for operational purposes, firms need a data set that is high-quality and delivered reliably from a provider they can trust. As they are going to put it into an automated, day-to-day recurring process, they need predictability around the availability and quality of the data.

However, if the data is for market exploration or research, the user might only want to use each data set once but are nevertheless likely to be more adventurous in finding new data sets that give them an edge in the market. The quality of the data and the ability to trust it implicitly are nevertheless still critically important.

 Inadequate existing approaches

There is a range of drawbacks with existing approaches to market data management and analytics. IT is typically used to automate processes quickly, but the downside is financial and market analysts are often hardwired to specific datasets and data formats.

With existing approaches, it is often difficult to bring in new data sets because new data comes in various formats. Typically, onboarding and operationalising new data is very costly. If users want to either bring in a new source or connect a new application or financial model, it is not only very expensive but also error-prone.

In addition, it is often hard for firms to ascertain the quality of the data they are dealing with, or even to make an educated guess of how much to rely on it.

Market data collection, preparation and analytics are also historically different disciplines, separately managed and executed. Often when a data set comes in, somebody will work on it to verify, cross-reference and integrate it. That data then has to be copied and put in another database before another analyst can run a risk or investment model against it.

While it is hard to gather data in the first place, to then put it into a shape and form, and place it where an analyst can get to work on it is quite cumbersome. Consequently, the logistics don’t really lend themselves to faster uptime or a quick process.

The benefits of big data tools

The latest big data management tools can help a great deal in this context. They tend to use cloud-native technology, so they are easily scalable up and down depending on the intensity or volume of the data. Using cloud-based platforms can also give firms a more elastic way of paying and of ensuring they only pay for the resources they use.

Also, the latest tools are able to facilitate the integration of data management and analytics, something which has proved to be difficult with legacy approaches. The use of underlying technologies like Cassandra and Spark makes it much easier to bring business logic or financial models to the data, streamlining the whole process and driving operational efficiencies.

Furthermore, in-memory data grids can be used to deliver a fast response time to queries, together with integrated feeds to streamline onboarding and deliver easy distribution. These kinds of feeds can provide last mile integration both to consuming systems and to users, enabling them to gain critical business intelligence that in turn supports faster and more informed decision-making

Maximising Return on Investment

In summary, all firms working in the finance or financial services sector should be looking to maximise their data return on investment (RoI). They need to source the right data and ensure they are getting the most from it. The ‘know your data’ message is important here because finance firms need to know what they have, understand its lineage and track its distribution, which is in essence good data governance.

Equally important, finance firms should also ensure their stakeholders know what data is available and that they can easily access the data they require. Ultimately, the latest big data management tools will make it easier for finance to gain that all important competitive edge.

Policymakers and regulators around the world have pointed to cyber threats from criminal and state actors as an increasing threat to financial stability. Last month, US Treasury Secretary Janet Yellen – along with finance ministers and central bank chiefs from the Group of Seven nations – conducted an exercise covering how G7 members will seek to cooperate in the hypothetical event of a significant, cross-border incident affecting the financial sector.

by Fabien Rech, EMEA Vice President, McAfee

Such concerns are widespread, with 80% of UK IT professionals anticipating a moderate or even substantial impact by increased demand for their services or products this holiday season. The extra demand is compounded by the reduced size of teams and greater online activity. With cyber threats to the financial industry front of mind, and organisations across the sector coming under scrutiny as to whether they are doing enough to protect themselves, this year’s peak season – and subsequent rise in online activity – is cause for concern.

While this paints a bleak picture, organisations can be proactive in defending their networks, data, customers, and employees, against the anticipated increase in holiday cybercrime by implementing certain security measures.

Using technology to bolster teams

Demand for cybersecurity is surging, and today there are a number of technologies that can help to bolster security measures, providing additional support for often stretched security teams. Threat intelligence can offer unique visibility into online dangers such as botnets, worms, DNS attacks, and even advanced persistent threats, protecting FS organisations against cyberthreats across all vectors, including file, web, message, and network.

In addition, taking a Zero Trust approach to security enforces granular, adaptive, and context-aware policies for providing secure and seamless Zero Trust access to private applications hosted across clouds and corporate data centres, from any remote location and device. This will be particularly useful as more employees choose to work remotely.

Prioritising employee awareness

Beyond technologies, the adoption of an awareness-first approach is vital. Proactive cybersecurity awareness training for all employees – not just those in the security team – is essential, especially when encountering holiday phishing emails. As the cyber threat is always evolving, so too must organisations – ensuring that their team’s knowledge and ability to identify, avoid and negate those threats also grow in turn.

This awareness-first strategy requires leaders to move away from a ‘breach of the month’ approach, instead of using proactive training measures to build security into the fabric of their organisation, breaking down siloes of threat and information intelligence across the business, so that all employees are aware of how they can contribute to the battle against cyberthreats during the peak period and beyond.

Some banks are already taking a proactive approach to testing employee understanding when it comes to cybersecurity, for example, resistance to spam or phishing emails, and knowing not to plug unknown USB keys into their laptop. If employees don’t appear to have sufficient knowledge of threats and best practices, they will automatically be required to take part in further training.

Other key steps to take in this proactive approach include increasing the frequency (and testing) of software updates, boosting the number of internal IT-related communications to keep everybody informed, and implementing new software solutions with due diligence.

Implementing a response plan

It’s also important to recognise that protective measures might not work 100% of the time. As hackers become ever more sophisticated, it’s vital for FS organisations to design a holistic, clearly communicable plan for if (and when) things do go wrong.

Developing a robust incident response plan could mean the difference between being able to respond and remedy a security breach in minutes rather than hours, ensuring the least amount of downtime possible. When asked, 43% of businesses reported suffering from downtime due to a cyber concern in the last 18 months – for 80% this happened during peak season and lasted more than 12 hours for almost a quarter (23%)

Again here, training forms a big part – making sure employees know what to do and who to inform when an incident does occur is at the heart of any effective response plan, as is encouraging a culture of honesty and transparency. An organisation in which employees are wary of acknowledging a mistake or informing someone of a possible accidental breach is not a secure one.

The year is full of challenging peak periods, from the public holidays at the end of the year to summer vacations and various religious/spiritual holidays. The need for vigilance has never been greater or more constant, and financial services organisations, in particular, have a need to protect the data and money of their customers, as well as the resilience of their own organisations.

By using technology, training, and incident response awareness, leaders in the sector can help to bolster teams against the increasing sophistication of cyberthreats, staying safe while staying connected. The peak season offers unique challenges, but ultimately the goal is to develop a resilient and adaptable organisation that can ensure security year-round, allowing employees to thrive, wherever they choose to work without having to worry about threats.

Buy Now Pay Later (BNPL) has seen a surge in growth since the start of the pandemic, but in order for the BNPL industry to sustain its development, it must be underpinned by comprehensive technology designed to optimise experience both for the merchant and the customer.

by Tom Myles, Chief Technology Officer, Deko

The most influential technology for business has been the rise of AI, machine learning and big data, having now permeated almost all sectors. Indeed, recent research highlights the key role that AI is expected to play in the future of fintech as a whole, with two-thirds of fintech firms predicting it to have more impact on the sector than any other tech in the next five years. As for BNPL products in particular, a forecast market of £26.4 billion by 2024 would mean it more than doubling in three years, so there is huge growth potential for AI to help unlock.

Payment optimisation

Lenders can use AI and machine learning to extract more value from BNPL platforms. Machine learning models, for instance, are built on data, and payments systems generate large quantities of data from which potential lenders can gain insights into consumer behaviour.

These data-driven decisions can streamline the process of matching the right lender to the right buyer at the right time, which will be all the more useful for unlocking the potential of platforms that operate on a multi-lender, multi-product basis. AI and machine learning models can match consumers to the right member of a multi-lender roster, enabling an increased provision of credit for consumers.

Delivering a rewarding experience for consumers is just as important as the flexibility of the financial technology involved, as increasingly tech-savvy consumers continually elevate the minimum standard of the online experiences in which they are prepared to engage. In particular, consumers are increasingly reticent to engage in elongated sign-up processes and demand a mobile-first approach. Offering a visibly streamlined, omnichannel payments process will therefore prove to be a vital differentiator in an ever more competitive market.

Moreover, empowering consumers and lenders to make more streamlined transactions will also be beneficial for merchants. AI-enabled technology will enhance the flexibility of retail finance products, enabling platforms to process more transactions at a higher pace. This, combined with reduced rates of basket abandonment, will help retailers increase their sales volume.

Fraud prevention

While merchants may relish the prospect of driving more sales, machine learning can also support them in another critical area that may be somewhat sobering to consider. As commerce continues to migrate online, the threat of fraudulent transactions looms larger than ever.

Leveraging AI and machine learning means that payment platforms can learn to recognise patterns in consumer behaviour, based on analysis of the data generated from previous transactions, so even the smallest changes in behaviour can be identified. These data-driven insights will enable automated flagging of potentially risky transactions, which will reliably protect merchants, lenders and consumers from fraud.

Automating this process is vital in a world where online fraud is increasingly sophisticated. An older, rules-based model might be able to test for numerous different types of fraud that have been recorded previously, but the system would remain vulnerable to as yet undetected types of fraud.

Future of FinTech

Using technology both to streamline payments and to prevent fraud will require FinTech platforms to strike a delicate balance. Security is of course vital, but rigorous fraud prevention processes should not come at the expense of a streamlined, speedy checkout from the user’s point of view. Indeed, for fintech platforms as well as for users, improved security and streamlined payment processes will ideally go hand-in-hand.

FinTech firms must therefore invest due consideration as well as resources into AI-enabled functionality for their platforms. And this investment has already proven well worth making. Given the benefits for both merchants and lenders, it is perhaps unsurprising that 83% of financial services professionals agreed that “AI is important to my future company’s success”. Indeed, these forward-thinking technologies and firms are integral to the development of the payments sector.

Instant payment processing is expected by today’s customers and clients, and as the world of business adapts to hybrid work, there is no better way to meet this need than with cryptocurrency. Cryptocurrencies allow businesses to make secure, instantaneous transactions while eliminating the middleman.

by Gabby Baglino, Digital Marketing Specialist, Bryt Software

This keeps everyone’s data safe and reduces transaction fees — and those savings can go toward growing the business rather than paying for unnecessary charges. Let’s take a closer look at why cryptocurrency is key to hybrid and remote work and the powerful new technology that makes it possible.

What is blockchain technology?

A blockchain is a public ledger that contains data from anywhere in the world. This entirely digital database is shared across a network that can include everyone in a company’s workforce, no matter where they’re physically located.

By decentralizing the information, this revolutionary technology ensures the security of financial transactions. When using the blockchain, no third parties are necessary.

Unlike a traditional database, the blockchain collects data and stores it in blocks. When one block is filled with data, it’s closed and linked to the previous block in the chain. Simply put, the blockchain is a chain of digital blocks, and once a block is closed the information it contains cannot be altered or deleted.

In the case of cryptocurrencies like Bitcoin, the blockchain’s decentralized nature allows it to be used as a storehouse of transaction information. All users have collective control over the data, and the changes are irreversible.

Financial transactions can be made securely among an unlimited number of team members located anywhere in the world. And when businesses need to store sensitive transaction information, the data records are protected from any kind of update, deletion, or destruction.

How businesses benefit from using cryptocurrency

Many business owners encourage crypto usage in everyday transactions. Let’s take a look at some ways that this type of transaction benefits small businesses and enterprises alike.

1. It’s more secure

Identity theft can lead to significant personal as well as corporate loss. When you make an online payment, you’ll need to enter credit card information. Your sensitive data is then sent through channels and stored in databases where it may be the target of a cyberattack.

Hackers can gain access to your account details and use them for unauthorized transactions even if your credit card numbers aren’t compromised.

The identification process made possible by the blockchain allows all your personal data to be encrypted in a single place and kept safe by advanced cryptography. With this method, identification is faster and more secure than it would be going through third parties that may be vulnerable to attack.

2. Smarter use of funds

Dependency on third parties can be a challenge for businesses that want to use their finances to expand. Lack of resources and external pressure can get in the way of taking appropriate and timely financial actions.

Though they may have plenty of great ideas, enterprises often cannot expand the way they want due to the lack of funds or the freedom to do what they want with their money. With blockchain technology, businesses have the liberty to use their finances effectively because they have direct access to their money.

3. No dependency on third parties

Cryptocurrency’s ability to allow businesses to avoid the middleman is a primary reason why cryptocurrency is attractive for companies of all sizes.

When a business depends on banks or other payment gateways, each transaction comes with a percentage fee, usually between 1% and 4%. Transaction fees can add up quickly, particularly for larger corporations. This can reduce the efficiency of the business in the long run.

Transaction fees can be crippling for smaller businesses, so it’s natural for small to midsize businesses to turn to cryptocurrency for relief. With blockchain technology, businesses can transact with global markets with little concern about exchange rates and processing fees.

Thanks to the technology, buyers and sellers can now communicate with each other directly without having to go through a bank. As a result, many businesses can afford high discounts to draw customers who can pay using cryptocurrency.

A study conducted in 2020 reports that 40% of customers who pay using digital cryptocurrency are new to the sellers. Clearly, the adoption of cryptocurrency as a mode of payment has increased the client base for many enterprises.

Cryptocurrency and the hybrid work era

As industries find ways to adapt to the explosion of work from home (WFH), the importance of paying employees in cryptocurrency has become central to the conversation. Thanks to remote work, businesses can cultivate hybrid teams, recruiting the best talent from anywhere in the world to work virtually with their in-office team members.

As fiat currencies are subjected to inflation and processing fees, among other things, cryptocurrencies make it easier for companies to pay their employees. When the employees get a combination of digital and fiat currencies as payment, they can later convert the crypto into fiat. 

Workers who are paid in cryptocurrencies have more control over their cash. And since the process uses effective encryption via blockchain technology, hackers have no access to sensitive data.

As hybrid work has become established, digital payment methods are also becoming acceptable in modern society. As an entirely online mode of payment, cryptocurrency offers an advantage over other payment methods with convenience as well as security.

Remote work isn’t going away anytime soon. As hybrid work cements its place in the world of work, digital currencies ensure that businesses run smoothly by offering the best solution to conduct transactions. Hybrid work and cryptocurrency will go hand-in-hand for data security and ease of use in the coming years.

Trade surveillance departments are under intense pressure from regulators to catch trade market abuse and fraudulent activity. But monitoring is becoming increasingly complex.

by Paul Gibson, Business Development Director, KX

Financial institutions must monitor activity relevant to their specific business, this means checking for market abuse, fraud, market disruption and fair practice as well as more malign abuses such as money laundering to support criminal activities like terrorism and people trafficking. This often means analysing vast amounts of both historical and real-time data, in a variety of formats from trade data to electronic communications. Analysts are becoming weighed down by large amounts of alerts and investigations, many of which prove to be unnecessary when other factors are considered.

To deliver a successful trade surveillance programme that satisfies the rigour of the regulators and the efficiencies demanded by the business, a consolidated approach is required. It must be effective across all lines of business for the detection of emergent, systemic and often unknown risk, and take a proactive approach to make sense of all of the interactions, dependencies, changes, patterns and behaviours across the entire trade lifecycle.

Cross-Product Analysis

Organisations need a platform that can process vast amounts of data from multiple streams in real-time, allowing users to make decisions on alerted behaviours much more effectively with significantly greater efficiency. This means using cross-product analysis to identify errors, automated techniques to reduce false positives and machine learning to extract insights from both historical and real-time data.

Traditional instrument-by-instrument trade surveillance techniques do not typically extend their analysis to related products. This means that in certain areas, such as credit and rates, the links between the topics and how they are affecting one another go unseen. This is opposite to risk management techniques across the same technologies where trade dependencies are closely monitored.

As such, it is important to incorporate risk management elements, such as benchmark and sensitivity measures to help identify potential abuse over a range of instruments. This enables products to be broken into their risk fundamentals and effectively ‘look through’ to the underlying securities in an analysis. In looking for evidence of manipulation of a Financial Risk Advisor (FRA), for example, the analysis may extend to monitor both futures and interest rate swaps too.

Reducing False Positives

The more information available to businesses means the more insightful judgements can be made. In regard to false positives, the presence of surrounding data can help contextualise results by automatically classifying high volumes of alerts. Analysis can then determine which are material and which are not. False positives reduction techniques fall into three areas:

• Data Filters – Filtering out specific data or activity that may not be applicable. For example, excluding immediate-or-cancel (IOC) orders from Spoofing profiles.
• Use of Dynamic Thresholds/Benchmarks – Replacing static thresholds with automatically adjusting parameters that reflect evolving market conditions and changing behaviours, not only of individual traders but across the market.
• Alert Feature Overlays – Including surrounding factors for context in assessing alert severity. For example, factoring in change in portfolio concentration when monitoring potential insider trading.

When used together, these factors help avoid unnecessary and time-wasting alerts that distract analysts from the more important and pressing investigations. Thereby, optimising both operational efficiencies and effectiveness for mitigation of true risks.

Future of Trade Surveillance relies on Machine Learning

From calibration to error reduction, machine learning enables a variety of business practices to be improved. Detection rates can be continuously refined using a blend of supervised learning, unsupervised learning and feature extraction techniques from the historical data store.

Supervised learning uses analyst feedback and assessment of historical results to train models and improve their accuracy. Unsupervised learning works on its own to discover the inherent structure of unlabelled data, using techniques like One-Class Support Vector Machines (SMVs) to detect anomalies to help classify results based on distributions and similarities.

SVMs establish normal behaviour by learning a boundary and then adding a score to the results, based on their distance from that boundary. This adjustment can then guide analysts on what investigations to prioritise. Indeed, the benefits of AI and machine learning are well documented, but their application for improving detection rates in trade surveillance is limited.

Regulators are still hesitant to allow machines to determine whether an activity is suspicious or not. This means that the majority of what we are seeing is a supervised learning approach. However, the regulatory landscape continues to evolve and the demand for real-time decision-making is mounting. Therefore, organisations will need to make a shift in mindset and capitalisation of narrow AI with unsupervised machine learning if they are keen to detect fraud effectively and accurately.

As a result of the ever-evolving market abuse tactics being detected, and which need to be prevented, the requirements for strong trade surveillance are more demanding now than ever. For firms, this increased complexity requires them to adopt a consolidated solution that delivers accurate insights when it’s most valuable – at scale both historically and in real time, enabling users to analyse data at a breadth and scale that wasn’t previously possible.

The flexibility of a high-performance streaming analytics platform is a game-changer for real time intervention where necessary and the timely flagging of abnormal behaviour based on large amounts of historical data. By using this technology, firms can take a proactive approach in their response to abnormal behaviour in as quick as microsecond, instead of reacting when it is too late. By doing so, firms can work to improve detection rates and make significant savings through fewer false positive cases and ensure operational efficiency is met.

For several years, the financial sector across the world has been undergoing a digital transformation. In a socially distanced world, the pandemic created more opportunities to innovate new digital financial services, while also uncovering the gaps in the Indian financial system, such as the inaccessibility of credit to Micro, Small, and Medium Enterprises (MSMEs). MSMEs are vital cogs in the growth engines of the Indian economy, contributing to about 30% of the GDP.

by Lalit Mehta, Co-founder & CEO, Decimal Technologies

However, during the pandemic, numerous MSMEs have suffered due to the absence of access to credit. The MSME credit gap approximately amounts to a monumental $240 billion due to traditional institutions’ lack of flexibility and their inability to effectively leverage the available user data and viably reach the semi-urban and rural areas.

This, however, is not just a problem, but also an opportunity. There is immense potential in the MSME segment for banking institutions as they keep adopting digital and tech innovations. As we step into the year 2022, let’s look at how this opportunity might play out within the financial sector.

Open Banking

Open banking is a solution that is emerging as a way to completely transform the way credit is disbursed, making it more suited to the financial situations of MSMEs. Open banking refers to a system where banks and other financial institutions allow third parties, such as fintech companies, to access user data via secure Application Program Interfaces (APIs). Not only can the APIs enable fintech partners to build new services that are more efficient and accessible, but also allow traditional financial institutions to offer experiences fit for the digital age.

Open APIs have the capability to help financial institutions digitise the process of lending and address the growing credit demand. They can also help with some much-needed customisation in the offerings, thereby catering to specific needs that might not be addressed in their entirety by a single legacy product. Artificial Intelligence (AI)-based solutions offer flexibility that suits the borrowers’ needs that might not always be feasible for traditional processes to identify or address.

Risk Assessment

The value of approved and disbursed loans is mainly determined by how an individual or business is likely to pay it back. This is why risk assessment, or determining how likely an individual is to default is critical for the entire sector, and this is where AI and Machine Learning (ML) can change the game. The AI/ ML components employed by FinTech firms can match the customer with the lender without minimal to zero manual intervention, solving one of the key problems of the lending industry- that of risk assessment. This is done with the assistance of detailed, user-friendly credit assessment memos which allow lenders to practice controlled yet faster risk assessment.

With the help of AI and ML, banks can understand how an individual’s or a company’s recent financial behaviour deviates from past behaviour, and therefore get early insights into potential causes of concern. In this scenario, having early insights enables financial providers to take action with a relevant response – i.e., reassessing the approved loan amount or declining a loan.

Maximising Profits

For years, banks and other lenders have been using computer systems to automate more and more of the loan process. With the massive growth witnessed by businesses on the back of new-age technology, many institutions are now trying to fully automate the process. Adoption of AI results in an enhanced borrower experience and assists in making informed decisions with utmost certainty. It eliminates administrative expenses and delays to maximize the amount of profit for every loan created. Removing human bias, decisions will increasingly be based on verified customer data like their monetary status and accuracy, giving little room for error and helping businesses focus on other aspects of the lending process that still require human attention.

The banks will also have the liberty to consider a more proactive approach towards the onboarding of new customers. During the loan application phase, AI and ML are often used to anticipate credit needs by analyzing credit line usage and understanding historical data patterns. For instance, an agricultural business is likely to have seasonal credit needs; these needs can be modelled to understand typical versus atypical patterns.

Better Banking Experience

An increase in the integration of AI and ML will also mean the elimination of human intervention. Decisions made by humans are almost always influenced by biases which may end in either a poor experience for the customers or losses in terms of loan frauds for financial institutions. AI-driven tools run the available data against a group of rules to work out the borrower’s acceptability, thereby speeding up the process, and also ensuring security for the institution.

By understanding how a company’s recent financial behaviour deviates from past behaviour, banks can detect or create opportunities for expanding their business relationship with the customer – or get early insights into potential causes of concern. In both these scenarios, having early insights enables financial providers to take action with a relevant response – i.e., extending credit proactively or declining a loan.

Conclusion

2022 is set to witness a further increase in the adoption of AI and ML. This will lead to a bridging of the credit gap that the MSMEs are suffering from, resulting in further bolstering of the economy. This will also exponentially enhance customer experience while cutting down on the risks. This New Year will be a better year for banking.

While disruption from the pandemic has highlighted many opportunities for development across multiple industries, it has especially emphasised the need for digital transformation within the financial services market.

by Hans Tesselar, Executive Director, BIAN 

At the beginning of the pandemic, financial institutions realised what it meant to be truly digital. Research from EY found that 43% of consumers changed the way they banked due to Covid-19 favouring a more digital approach. Almost overnight, banking organisations were forced to shift their focus towards becoming more agile, resilient and, above all, digital.

Despite the importance of transformational efforts, the financial services industry continued to come up against obstacles, highlighting the need for urgent industry action.

Digital-First Customer 

The financial services sector has realised that without the comprehensive digital infrastructure necessary for today’s environment, they are unable to bring services to market as quickly and efficiently as they would like – and need. The extensive use of legacy technology within banks meant that the speed at which these established institutions could bring new services to life was often too slow and outdated.

This challenge is also complicated by a lack of industry standards, meaning banks continue to be restricted by having to choose partners based on their language and the way they would work alongside their existing ecosystem. This is instead of their functionality and the way they’re able to transform the bank.

To move forward into the ‘digital era’ and continue on the path to true digitisation, banks need to overcome these obstacles surrounding interoperability. Additionally, with today’s digital-first customer in mind, financial institutions need to take advantage of faster and more cost-effective development of services. Failing to provide these services may force customers to take their business elsewhere.

One thing is certain, consumers will continue to prioritise organisations that can offer services aligned to both their lifestyle and needs.

Coreless Banking 

The concept of a ‘Coreless Banking’ platform is one that supports banks in modernising the core banking infrastructure.

This empowers banks to select the software vendors needed to obtain the best-of-breed for each application area without worrying about interoperability and being constrained to those service providers that operate within their language. By translating each proprietary message into one standard message model, communication between financial services is, therefore, significantly enhanced, ensuring that each solution can seamlessly connect and exchange data.

With the capacity to be reused and utilised from day one, and the ability to be used by other institutions, Coreless Banking provides these endless opportunities for financial services industries to connect, collaborate and upgrade.

The Future is Bright

It’s clear that the world is facing a digital awakening, and banks are eager to jump on board. Ensuring that the rapidly evolving consumer has everything they need in one place has never been more essential, and the time to enhance the digital experience is now.

The payments industry has continuously evolved over the last few decades and the pace of change has only increased. Technology has contributed significantly to delivering payment services to the consumer. These changes are driven by the need to increase the lending portfolio reliably with a seamless customer experience.

by Aravind Irodi, Senior Vice President, Head of Practices, Attra, a Synechron company

The recent incarnation of Buy Now Pay Later products – as available against other financial services solution such as credit cards — to a standalone solution financed by FinTechs has taken the industry by storm. This has led to a number of new players, such as Klarna and Afterpay amongst others, achieving significant growth in lending volumes. These FinTechs are lending to the new age population and taking market share away from the traditional card players on short-term credit. The FinTechs are underwriting the risk and it has shown to be a profitable initiative for the now larger players. Additionally, other well-known retail platforms (like Amazon.com and Walmart) have recognised the vast opportunity and are implementing this phenomenon for their customers.

Traditional payment service providers (a.k.a. card issuers) have an opportunity to capitalise on their existing customer relationships to provide revolving credit in a Buy Now Pay Later fashion, independent of card networks. They can use their existing accounts receivable infrastructure to provide this service with changes at the point of sale (POS) to provide the payment option and direct settlement with merchants. These payments providers possess the added advantage of having an existing customer base and credit assessment infrastructure in place under which to make credit allocation decisions. Accelerated deployment of market-ready solutions is the key to ensuring that market share is retained as FinTechs disrupt the marketplace and bigger, steadfast participants seize the opportunity.

In pursuit of superior seamless payment experiences

There are also innovations happening on the merchant side to make payment experiences seamless. A key global trend across large merchants in recent years has been to provision an omnichannel experience to its customers. This is now coming of age, with technology solution providers building out solutions to cater to this need.

Customer touch points have evolved with payment options, such as contactless cards, wallets and QR code-based payments. Each of these payment options have penetrated markets to varying degrees across the globe. Contactless payments have been mainstream in Europe and Australia for the last decade whilst playing catch-up in other markets. QR code payments have had significant acceptance in the Asia Pacific market and are now finding greater acceptance across the globe. The form factor is also evolving with the usage of wearables, such as smart watches and voice enabled payments from devices such as Alexa, finding their way into the payment ecosystem. Digital wallets, an innovation that has been in existence since the emergence of PayPal, is now gaining market share in super app ecosystems with each of the major players coming up with their own proprietary wallets. Card issuers are also moving towards a digital form factor, particularly for their prepaid cards.

In an attempt to significantly improve the shopping experience with a completely seamless payments interface, Amazon has now pioneered ‘just walk out’ technology under the brand Amazon Go. It is now offering this technology to other retailers with the market expected to evolve in the near term. These innovations are leading to the co-existence of various payment options across the globe. The hybrid ecosystem is largely due to market segmentation, not just by geography but also customer demographics and investments for large scale deployment in larger markets.

Constant ecosystem technology updates   

These continued changes in improving customer experience and making payments seamless has meant that merchants and their technology service providers must constantly keep their systems up to date. Brick-and-mortar stores have a wide variety of POS infrastructures and increasing payment options mean constant updates to this ecosystem. The online payment options are relatively easier to change. Aggregated payment gateway service providers enable a faster deployment of the latest technologies for online payment service providers.

We foresee continued evolution of payments with an increasing focus on customer comfort and making payments as seamless as possible. This would require financial services organisations to enhance their technology infrastructure in order to support these advancements and in keeping pace with market leaders. The traditional payments service providers, such as card issuers, need to have a forward-looking business team looking to launch new product options for consumers and, equally as important, is to have a technology team to enable a fast time to market.