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How Cloud-Native Infrastructure is Reshaping Core Banking System

September 26, 2024

In the digital age, banking has rapidly evolved, with customers demanding seamless, 24/7 services. Many banks remain burdened by legacy core banking systems that limit their ability to meet these demands. These older systems often struggle to integrate modern technologies such as artificial intelligence (AI), machine learning (ML), and real-time analytics, resulting in increased operational costs and reduced agility.

In this rapidly evolving landscape of financial services, core banking systems need significant transformation. The advent of cloud-native infrastructure is at the forefront of this revolution, offering unprecedented agility, scalability and efficiency.

What is Cloud-Native Infrastructure?

Cloud-native infrastructure is a set of technologies and practices that enable the development and deployment of applications in the cloud. It is characterized by microservices, containers, and continuous delivery pipelines.

Microservices: Microservices are small, independent services that work together to form a larger application. This modular approach makes it easier to develop, test, and deploy applications.

Containers: Containers are lightweight, portable units of software that package up an application and its dependencies. This makes it easier to move applications between different environments.

Continuous delivery pipelines: Continuous delivery pipelines automate the process of building, testing and deploying applications. This helps to ensure that applications are always up-to-date and reliable.

How Cloud-Native Infrastructure is Reshaping Core Banking Systems

Moving core banking systems to a cloud-native architecture offers numerous advantages.

Enhanced Security: Security is a top priority for any financial institution. Cloud-native infrastructure offers advanced security features, such as automated patch management, encryption and continuous monitoring. These capabilities help banks protect sensitive data and comply with stringent regulatory requirements.

Faster Time-to-Market: In the competitive banking sector, the ability to quickly launch new products and services is a significant advantage. Cloud-native systems enable rapid development and deployment cycles, allowing banks to respond swiftly to market changes and customer needs. This agility fosters innovation and helps banks stay ahead of the competition.

Scalability and Flexibility: Cloud-native infrastructure allows banks to scale their operations effortlessly. Whether it’s handling a surge in transactions during peak times or expanding services to new regions, cloud-native systems can dynamically adjust to meet demand. This flexibility is crucial for banks looking to innovate and grow without being hampered by their IT infrastructure.

Real-World Applications

Emirates NBD, one of the largest banking groups in the Middle East, has been at the forefront of adopting cloud-native technologies. The bank has implemented a cloud-native core banking system to enhance its digital banking services. This transition has enabled Emirates NBD to offer more personalized and responsive services, improve operational efficiency and rapidly deploy new features to meet customer demands.

Mashreq Bank, another major player in the GCC region, has leveraged cloud-native infrastructure to drive its digital transformation. By adopting microservices architecture and containerization, the bank has been able to scale its operations dynamically and enhance its customer experience. The bank’s cloud-native approach has also facilitated the integration of advanced analytics and artificial intelligence, enabling more informed decision-making and innovative product offerings.

Capital One is another notable example. The bank has been a pioneer in adopting cloud-native infrastructure, migrating its entire data centre operations to the cloud. This move has not only reduced operational costs but also enhanced the bank’s ability to innovate. Capital One now uses cloud-native technologies to leverage big data and machine learning, providing customers with tailored financial advice and fraud detection services.

State Bank of India (SBI), the largest public sector bank in India, has adopted cloud-native technologies to support its digital transformation initiatives. SBI’s cloud-native infrastructure has enabled the bank to handle large volumes of transactions efficiently, enhance its cybersecurity measures, and offer a seamless banking experience to its customers. The bank’s cloud-native approach has also facilitated the integration of new technologies such as blockchain and artificial intelligence.

 

Cloud-native infrastructure is not just a technological trend; it’s a strategic imperative for banks. By embracing cloud-native technologies, banks can position themselves for long-term success in a rapidly evolving digital landscape. As the banking industry continues to innovate, cloud-native infrastructure will play a pivotal role in shaping the future of core banking systems.

CategoriesExclusive

Redefining Wealth Management: The FinTech Paradigm

September 13, 2024

The WealthTech landscape is experiencing a paradigm shift, fuelled by the innovation in the FinTech sector. As decentralized finance (DeFi) platforms upend traditional models and personalized financial planning becomes more prevalent, we are witnessing a rapid evolution towards a more inclusive and efficient financial ecosystem.

Central to this transformation is the shift from generic financial strategies to highly tailored financial planning. Leveraging cutting-edge technologies such as artificial intelligence (AI) and big data analytics, FinTech platforms offer bespoke financial solutions. These platforms sift through extensive data sets to tailor financial strategies that align with individual profiles, encompassing risk tolerance and long-term goals. This personalized methodology not only heightens client satisfaction but significantly bolsters the prospects of meeting specific financial ambitions.

The democratization of financial services is another significant aspect of this revolution. Traditionally, expert financial advice was a privilege of the few. Now, FinTech innovations, including robo-advisors, are breaking these barriers by offering cost-effective, automated investment solutions to a wider audience. This shift not only broadens access to financial advice but also promotes financial inclusion globally, thus creating new market opportunities and empowering a diverse range of economic participants.

Efficiency and cost-effectiveness continue to be significant benefits innovation in FinTech. Traditional wealth management entities are burdened by high operational costs rooted in manual processes and legacy systems. Through automation and technological advancements, new financial technology is streamlining these processes, curtailing human error, and reducing costs—enabling competitive pricing and allowing wealth managers to devote more time to strategic client engagement rather than mundane administrative tasks.

The future looks even brighter for wealth-tech. The integration of blockchain technology is set to offer transparency and security in financial transactions. Moreover, the growing domain of DeFi platforms will challenge the conventional norms of wealth management, presenting innovative investment avenues and complexities.

The synergy of FinTech and wealth management is crafting a financial landscape that is simultaneously more accessible, personalized, and efficient. As we move forward, the role of FinTech will be increasingly pivotal in sculpting the future contours of wealth management, benefiting consumers and the wider financial sector alike.

CategoriesAnalytics Big Data IBSi Blogs IBSi Flagship Offerings

Unlocking a digital future: how the finance industry can improve data quality

Deloitte’s recent predictions for the future of finance highlight the need for the finance industry to adopt the technology available to them in order to remain competitive. But for the finance sector to become truly digital, the quality of data is paramount.

By Baiju Panicker, Global CTO and Practice Head – Banking, Insurance and Financial Services at Altimetrik

Shifting to a truly digital mindset means adopting a digital business methodology that uses data to support and improve operations. However, if the data is low in quality, incomplete, or corrupted, this will make it near-impossible for the business to operate in an efficient and effective way.

Data quality critical in finance

Baiju Panicker, Global CTO and Practice Head – Banking, Insurance and Financial Services at Altimetrik
By Baiju Panicker, Global CTO and Practice Head – Banking, Insurance and Financial Services at Altimetrik

Low-quality or incomplete data can lead to poor lending, high-risk, flawed valuations and suboptimal trading. Ineffective targeting can also result from poor data, as can complaints, failures, and distorted insights.

In stark comparison, accurate data enables sound business decisions. High-quality data provides insight for analytics and efficient banking activities. It establishes greater integrity across operational analytics, fundamental to successful financial decisions and the overall success of the financial industry.

A great example of this is artificial intelligence (AI). AI is only as good as the data it accesses. It is crucial for financial firms to invest in data quality at the outset to enable successful digitisation, which in turn can boost competitiveness in the market and increase customer satisfaction.

Technology adoption critical

The adoption of technology is central to improving data quality. Leveraging various technologies to enhance data quality, such as automation tools for validation, AI for anomalies, and streaming analytics for real-time monitoring can ensure that only accurate and validated data is captured, improving the data quality immediately.

Data machine learning, blockchain, and natural language processing can help financial institutions improve their data quality and overall market performance by spotting inconsistencies, securing transactions, and extracting insights.

Without these building blocks, there is great potential for failure. Multiple client records can cause confusion, incorrect bills can damage trust, and customers and contracts may be lost.

Cleansing existing data is vital, but it is important to recognise that this cannot effectively be undertaken as a one-off project. Instead, it needs to be implemented as an ongoing activity to ensure overall business success. Alongside this ongoing process, businesses need to properly validate data as it comes in, such as automating data input and real-time monitoring to maintain a high standard of data throughout.

Utilising data stewards to monitor and address data quality issues gives a direct responsibility within the business to monitor data, clearly setting out the business’ intention to staff, customers and stakeholders that data quality is at the heart of the organisation and its operations.

Building a sustainable data quality framework

Undoubtedly, there will be lots of challenges that businesses face whilst undertaking this process. Focusing on a short-term goal – such as a single data cleanse – can be short-sighted and only create the same problems further down the line. Ensuring coordination across the business is key to success, which leads to greater accountability and removes silos from the process.

Machine learning and rule-based detection can support teams and help avoid any deviation from the prescribed style of data being captured. Text mining and natural language processing can help businesses analyse documents, call transcripts, and social media posts to identify semantic anomalies and outliers that indicate data quality issues. Alerts can then be set up to flag when issues emerge.

Ultimately, combining technology-driven detection with business-driven strategies for ongoing data quality improvement will enable businesses to be vigilant regarding poor quality or erroneous data being captured and utilised.

How to ensure quality

Proactive checking of data for errors and maintaining its quality is vital to the whole process of data quality, as early identification of problems helps to establish trust. Establishing a governance structure internally, where all parties are aware of and active in their roles, is fundamental both from a business perspective and also for stakeholders and customers.

Cross-functional data governance is important. It is not enough for each department to run its own checks and processes; it needs to be business-wide, with no silos or breaks in communication. This is where a Single Source of Truth (SSOT) is important. Rather than having multiple data locations that might not interact with other departments or processes, holding all the information centrally allows for better data accuracy and effective data cleansing across the whole business.

Overarching benefits of high-quality data

The potential benefits of improved quality of data to financial organisations are manifold. There is huge potential for increased revenue and cost savings through optimised data-driven decisions and operations. Data-driven activity is always more accurate, and data quality is central to this. The results are improved customer satisfaction and retention, with improved product offerings based on accurate findings.

From an operational perspective, management will see higher employee productivity with reliable data to work with, coupled with higher staff satisfaction. Through the integration of accurate, high-quality data there can be an increased use of automation and AI for more mundane tasks, enabling employees to work on more challenging and rewarding activity.

The finance industry is at a crucial juncture when it comes to digital adoption. Those who embrace digital adoption and intelligent ways of working through data and intelligent analytics will thrive, whilst those who lag behind will struggle to compete against competitors with a digital business mindset.

CategoriesAnalytics IBSi Blogs IBSi Flagship Offerings

Banks have the Generative AI advantage, but must overcome challenges to fully utilise its benefits

Jay Limburn, VP of AI Product Management, IBM
Jay Limburn, VP of AI Product Management, IBM

Despite the many challenges the industry has faced, the banking sector has continued to prioritise digital transformation and it is only accelerating quicker. Generative artificial intelligence (AI) is the latest in a wave of disruptive technologies that will drastically transform the financial services and banking industry.

By Jay Limburn, VP of AI Product Management, IBM

Many banks and financial institutions are as good as, if not better than most industries when it comes to technological maturity. We have been working on generative AI with banks for several years, and they have been experimenting with the operational advantages of AI across their business. The IBM 2023 CEO Decision-Making in the Age of AI report showed that 75% of CEOs surveyed believe the organisation with the most advanced generative AI will have a competitive advantage. However, executives are also concerned about the potential risks around security, ethics and bias.

Leaders are looking to fuel their digital advantage to drive efficiencies, competitiveness and customer satisfaction, but they have not been able to fully operationalise AI as they face key challenges around implementation.

The biggest challenge and opportunity…data

Banks are continuing to digitally innovate, and data has emerged as one of the biggest challenges to fully utilising generative AI across the industry. Platforms like ChatGPT caught people’s imaginations and created excitement in the sector. But while they rely on Large Language Models (LLM) to analyse vast amounts of data, the banks need to be able to choose from multiple models and embed their own data sets for analysis.

Instead of having one model to rule them all, banks will need to evaluate which models can be applied to their individual use cases. Banks are aware of the benefits generative AI can bring, so in place of summary capabilities of what the technology can do, they need to look at how to modernise different elements of their business. This requires models to be trained on the bank’s own data sets to get high-level accuracy and to fully operationalise the technology.

The amount of data is overwhelming many organisations, and banks are not excluded. To succeed, financial institutions will need to embed their own data into generative AI models to fully operationalise the technology.

Banks can help shape regulation and governance

One of the other key challenges facing banks with regards to generative AI is regulation and governance. As a new and emerging technology, regulators will not necessarily understand AI, so the natural inclination is to say we cannot use it. Equally, some models cannot explain why it has made a decision. For trust and compliance, financial institutions need to explain their decision-making process.

The more AI is embedded into organisations, the more important it is that leaders have a proactive approach to governance, which means having a legal framework to ensure AI is used responsibly and ethically, helping to drive confidence in its implementation and use.

But in order to help shape the AI regulatory environment and meet these requirements, banks need to take an active part in shaping the regulatory framework and move to models which can explain the decision-making process.

Generative AI will help not lead

The response we have seen from banks to generative AI has been phenomenal. As an industry, financial services and banking can lead the charge around AI regulation and explore new models to leverage their own data for better outcomes.

However, this isn’t without its challenges. Operationalising generative AI has proved difficult due to potential risks, compliance and evolving regulatory requirements, and concerns would be heightened as banks introduce their own data to AI models – which is why most generative AI use cases have so far focused on the customer care space.

Despite these challenges, banks have a huge opportunity to leverage generative AI, which will fundamentally change how we bank and how banks serve customers, and governance will play an active role in ensuring trust as we continue to explore the benefits of generative AI. Importantly, AI is here to help banks, not be the lead in most use cases.

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Embracing technology to navigate economic turbulence in the financial services sector

Guy Mettrick, VP, Financial Services at Appian
Guy Mettrick, VP, Financial Services at Appian

Today’s dynamic financial landscape has exposed the vulnerabilities of the financial services sector and shattered preconceived notions about banks’ regulatory resilience. The rapid collapse of once-revered institutions highlights the fragility of the banking sector in the face of economic turbulence and unforeseen market shifts.

With analysts scrambling to dissect the factors behind these failures, it is crucial to consider the broader implications for the financial services industry and the potential ripple effects on the overall economy.

Guy Mettrick, VP, Financial Services at Appian

Adaptive strategies for growth and innovation are becoming increasingly important amidst a background of stricter risk management, reduced lending, and increased regulation. To navigate the unpredictable path ahead that is defined by tightening regulatory frameworks and resource limitations, agility is key.

Balancing regulatory challenges

Mounting regulations driven by factors such as climate change and the push for enhanced compliance are forcing businesses leaders to reconsider their organisation’s strategic approach. The prominence of environmental, social, and governance (ESG) objectives in the financial services sector requires increased attention and significant investments in human resources and technology.

While these circumstances may lead to scaled-back growth aspirations, cost-cutting initiatives and deferred investment decisions, they also present transformative opportunities.

Leveraging technological advancements

During economic uncertainty, technology emerges as a powerful force within the financial services landscape. When it comes to expediting client onboarding, enhancing customer service, and facilitating seamless communication between financial institutions and their clients, automation proves indispensable. Automation enhances process efficiency and efficacy by eliminating manual tasks and minimising errors. Advanced technologies like artificial intelligence, robotic process automation, and process mining empower financial organisations to drive innovation within complex frameworks.

With automation, firms can facilitate real-time reporting and audits that provide tangible evidence of control effectiveness by embedding risk controls directly into their processes. In an era of increasingly stringent regulatory frameworks, this proactive approach to compliance proves invaluable.

The rise of data fabric

One emerging trend is the adoption of enterprise-wide data fabric, project by Market Watch to grow from $1.71 billion in 2022 to $6.97 billion by 2029. Data fabric streamlines the consolidation of data from various systems, a process that has traditionally been challenging and costly. This integration eliminates the need for data migration – a critical prerequisite for successful process automation.

Data fabric seamlessly connects and harmonises existing databases. This breaks down data silos and enables a cohesive and compliant framework that consolidates all relevant data sources. Within the financial services sector, this technology facilitates easy access to vital components such as risk governance policies and customer data.

Financial service providers must adopt adaptive strategies and embrace technology to effectively manage risks, regulations, and growth during an economic downturn. Regulation should not be perceived as a burden. Financial institutions should view technology, particularly process automation, as a catalyst for growth. Automation and data fabric enable these organisations to navigate complexities, streamline operations, and enhance customer experiences. Rather than succumbing to challenges, financial service providers can leverage technology to foster innovation, ensuring resilience in the face of economic uncertainty.

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What’s next in digital transformation in Europe

In Broadridge’s third annual Digital Transformation and Next-Gen Technology Study, 500 C-level executives and their direct reports across the buy side and sell side from 18 countries were surveyed

Mike Sleightholme, President, Broadridge International
Mike Sleightholme, President, Broadridge International

Mike Sleightholme, President, Broadridge International

On average, respondents’ firms control estimated assets of $121 billion. More than half agreed that digital transformation is currently the most important strategic initiative for their company, and the proportion of IT budgets allocated to digital transformation has increased to 27% on average, up from 11% last year. A further 71% of global respondents also say AI is now significantly changing the way they work.

The biggest increase in technology investment from European firms in the next 2 years will be allocated to cybersecurity – with respondents saying they plan to increase spending by 29% by 2025. This level of backing is followed closely by investments into cloud platforms and applications. Firms are ‘lifting and shifting’ legacy systems in favour of cost-effective, cloud-based infrastructure with microservices and APIs at the core.

Spending on data analysis and visualisation tools is planned to increase by 26% in the next 2 years. As it stands, too many firms are relying on fragmented data sets that could offer valuable insights if they were brought together and combined with powerful analytics solutions. The top driver for these investments is improved customer acquisition and retention. As market competition increases, the benefits that next-gen technologies can bring to the end-consumer are one of the most significant ways that firms may differentiate themselves from one another.

The second biggest factor in the decision-making process are cost savings and efficiencies. As next-gen technologies mature, the financial benefits become more tangible, making it easier to define a business case for investment.

Finally, speeding up the time it takes to bring new products to market is a priority for European firms and ranks as the third biggest driver for investments. This agility allows firms to take advantage of short-lived opportunities to gain market share in new asset classes or client segments as the pace of change accelerates.

The biggest challenge cited by European firms is insufficient budget for innovation. Particularly against today’s economic backdrop, firms are feeling hesitant to invest money into new projects. The second biggest challenge is staff resistance to constant change. Gaining buy-in from the teams that will be using the technology can be as important as buy-in from the C-suite approving investments. Education is important – firms must ensure their teams properly understand why these technologies are necessary, the efficiencies they can create, and how they will help the team, the business, and clients. The third most prevalent challenge for European firms is ongoing market and economic disruption. Against a backdrop of geopolitical tensions, recession fears and persistent inflation, it can be difficult for business leaders to focus their attention on technology investments.

Digital transformation is still at the top of the C-suite agenda, but it is also entering a new phase driven by more powerful technology. Widescale adoption of generative AI, as well as growing maturity in blockchain and DLT, will drive a new wave of exponential change. Other nascent technologies such as quantum computing and the metaverse are on the horizon.

When asked about the longer-term future, 65% of European firms believe that blockchain and DLT will become the core of financial markets infrastructure in 10 years’ time. Nearly a third believe that the metaverse will become a key channel for client interaction within the next 10 years. However, firms said they only plan to increase investment in the metaverse by 4% over the next 2 years, indicating a wait and see approach.

This is an exciting time for the financial services industry, adapting to the rapid pace of change may pose huge challenges for business and society, senior leaders should keep a firm eye on the opportunities created by digital and next-gen technologies as they evolve.

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Transforming financial lnclusion through AI and Machine Learning

Rajat Dayal, CEO, Yabx.
Rajat Dayal, CEO, Yabx

The financial industry is undergoing a profound transformation, largely driven by the growing influence of Artificial Intelligence (AI) and Machine Learning (ML). Within this dynamic landscape, the FinTech sector has emerged as a trendsetter, spearheading the adoption of AI and ML technologies.

By Rajat Dayal, CEO, Yabx

These advancements are redefining sustainable finance, particularly in terms of financial inclusion, by breaking down barriers that have traditionally hindered access to banking services, such as loans and investment opportunities for the unbanked population.

Credit Scoring and Risk Assessment

Yabx’s innovative use of AI/ML algorithms on raw data has led to the creation of 15,000 features for comprehensive financial profiles of borrowers, highlighting their commitment to data-driven lending. This transformation is pivotal, with credit scoring and risk assessment at its core. These systems leverage a diverse range of data to assess an individual’s financial reliability, effectively reducing one of the key risks associated with lending. Machine learning models have elevated the standards of evaluating an individual’s creditworthiness. This innovative approach empowers banks to expand their portfolios without compromising their risk tolerance, offering loans with a more refined risk management strategy.

Recommendation Engines

In a world where choice is paramount, AI-driven recommendation engines come to the forefront. These engines utilise customer behaviour patterns to provide tailored suggestions for financial products and services, especially loan products that align with the unique needs of each consumer. This bespoke process significantly increases the likelihood of successful loan applications, offering a more personalised and user-friendly experience.

Enhancing Customer Segmentation and Personalisation

AI and ML algorithms are now increasingly employed to enhance customer segmentation and personalisation. The ability to categorise consumers based on their financial behaviours and preferences allows for the provision of tailored loan products with unparalleled precision. This level of personalisation is particularly valuable for microbusiness owners, as it reduces the traditional financial bureaucracy, making borrowing more accessible.

Customer Insights and Market Research

AI and ML technologies offer analytical power, enabling organisations to gain deep insights into market trends and customer behaviour. This foresight equips businesses with the ability to adapt to market shifts and cater to the evolving financial needs of their diverse customer base, ensuring they remain competitive.

Automated Customer Onboarding

Efficiency and customer accessibility are at the forefront of the FinTech process. AI-driven solutions automate identity verification and Know Your Customer (KYC) procedures, streamlining the customer onboarding process. This automation ensures that borrowers can promptly access the financial support they need, free from cumbersome administrative delays.

In Action

An exciting example of AI and ML in action is Zed-Fin Loans, powered by Yabx, a pioneering sustainable banking initiative in Zambia driven by a powerful tri-party LAAS partnership. This partnership allows parties from three adjacent industries to work together to bring micro loans to the market in Zambia. Zed-Fin Loans is a testament to the transformative power of collaboration, technology, and innovation. Their success is a resounding endorsement of AI and ML algorithms, displaying their positive impact on Zambia’s financial landscape.

In conclusion, AI and ML are revolutionising the financial sector, making it more inclusive, efficient, and customer centric. These technologies are breaking down barriers and setting new standards, as demonstrated by the success of initiatives like Zed-Fin Loans in Zambia. The future of finance in Zambia and around the world looks to be very promising, thanks to the collaborative power of technology and innovation.

CategoriesAnalytics Artificial Intelligence IBSi Blogs

Can ChatGPT help fight cybercrime?

Open AI’s ChatGPT has taken the world by storm, with its sophisticated Large Language Model offering seemingly endless possibilities. People have put it to work in hugely creative ways, from the harmless scripting of standup comedy to less benign use cases, from AI-generated essays that pass university-level examinations to copy that assists the spread of misinformation.

Iain Swaine, Head of Cyber Strategy EMEA at BioCatch

Iain Swaine, Head of Cyber Strategy EMEA at BioCatch
Iain Swaine, Head of Cyber Strategy EMEA at BioCatch

Chat GPTs (Generative Pretrained Transformers) are a deep learning algorithm that generates text conversations. While many organisations are exploring how such generative AI can assist in tasks such as marketing communications or customer service chatbots, others are increasingly questioning its appropriateness. For example, JP Morgan has recently restricted its employees’ use of ChatGPT over accuracy concerns and fears it could compromise data protection and security.

As with all new technologies, essential questions are being raised, not least its potential to enable fraud, as well as the power it may have to fight back as a fraud prevention tool. Just as brands may use this next-gen technology to automate human-like communication with customers, cybercriminals can adopt it as a formidable tool for streamlining convincing frauds. Researchers recently discovered hackers are even using ChatGPT to generate malware code.

From malware attacks to phishing scams, chatbots could power a new wave of scams, hacks and identity thefts. Gone are the days of poorly written phishing emails. Now automated conversational technologies can be trained to mimic individual speech patterns and even imitate writing style. As such, criminals can use these algorithms to create conversations that appear to be legitimate but which mask fraud or money laundering activities.

Whether sending convincing phishing emails or seeking to impersonate a user and gain access to their accounts or access sensitive information, fraudsters have been quick to capitalise on conversational AI. A criminal could use a GPT to generate conversations that appear to be discussing legitimate business activities but which are intended to conceal the transfer of funds. As a result, it is more difficult for financial institutions and other entities to detect patterns of money laundering activities when they are hidden in a conversation generated by a GPT.

Using GPT to fight back against fraud

But it is not all bad news. Firstly, ChatGPT is designed to prevent misuse by bad actors through several security measures, including data encryption, authentication, authorisation, and access control. Additionally, ChatGPT uses machine-learning algorithms to detect and block malicious activity. The system also has built-in safeguards against malicious bots, making it much harder for bad actors to use it for nefarious purposes.

In fact, technologies such as ChatGPT can actively help fight back against fraud.

Take Business email compromise fraud (BEC). Here a cybercriminal compromises a legitimate business email account, often through social engineering or phishing, and uses it to conduct unauthorised financial transactions or to gain access to confidential information. It is often used to target companies with large sums of money and can involve the theft of funds, sensitive data, or both. It can also be used to impersonate a trusted business partner and solicit payments or sensitive information.

As a natural language processing (NLP) tool, ChatGPT can analyse emails for suspicious language patterns and identify anomalies that may signal fraud. For example, it can compare email text to past communications sent by the same user to determine if the language used is consistent. While GPT will form an essential part of anti-fraud measures, it will be a small part of a much bigger toolbox.

New technologies such as GPT mean that financial institutions will have to strengthen fraud detection and prevention systems and utilise biometrics and other advanced authentication methods to verify the identity of customers and reduce the risk of fraud. For example, financial organisations already use powerful behavioural intelligence analysis technologies to analyse digital behaviour to distinguish between genuine users and criminals.

In a post-ChatGPT world, behavioural intelligence will continue to play a vital role in detecting fraud. By analysing user behaviour, such as typing speed, keystrokes, mouse movements, and other digital behaviours, behavioural intelligence will aid in spotting anomalies. These can indicate that activities are not generated or controlled by a real human. It is already very successfully being used to spot robotic activities which are a combination of scripted behaviour and human controllers.

For example, a system can detect if a different user is attempting to use the same account or if someone is attempting to use a stolen account. Behavioural intelligence can also be used to detect suspicious activity, such as abnormally high or low usage or sudden changes in a user’s behaviour.

As such, using ChatGPT as a weapon against fraud could be seen as an extension of these strategies but not as a replacement. To counter increasingly sophisticated scams, financial service providers such as banks will need to invest in additional control such as robust analytics to provide insights into user interactions, conversations, and customer preferences and comprehensive audit and logging systems to track user activity and detect any potential abuse or fraudulent activity.

And it’s not all about fraud prevention. Financial institutions should also consider how they use biometric and conversational AI technologies to enhance customer interactions. Such AI-driven customer service platforms can ensure rapid response times and accurate resolutions, with automated customer support services providing quick resolutions and answers to customer queries.

Few world-changing technologies arrive without controversy, and ChatGPT has undoubtedly followed suit. While it may open some doors to criminal enterprise, it can also be used to thwart them. There’s no putting it back in the box. Instead, financial institutions must embrace the full armoury of defences available to them in the fight against fraud.

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Why FinTech M&A in the UK is on the up and up 

The UK FinTech sector will experience an upswing in M&A towards the end of 2023, as companies look to consolidate their positions in the market and take advantage of the potential for growth and innovation.

By Konstantin Dzhengozov, Co-Founder and Chief Financial Officer at Payhawk 

By Konstantin Dzhengozov, Co-Founder and Chief Financial Officer at Payhawk 
Konstantin Dzhengozov, Co-Founder and Chief Financial Officer at Payhawk

While headwinds such as the turbulent geopolitical landscape, volatile stock markets, and rising interest rates and inflation have meant both companies and investors have remained cautious throughout Q1 and into Q2, pressure is mounting for them to complete transactions.

According to data from Prequin Pro, this is particularly pertinent to private equity firms that are sitting on a record level of $1.96 trillion (about £1.5 trillion) of dry powder. Thus, we will soon see a switch out of defensive cash strategies and into M&A. Figures from Ernst & Young’s latest CEO Outlook, for example, show that 50% of UK CEOs are planning to make acquisitions in the next 12 months and 67% are considering joint ventures.

VC funds, on the other hand, will not have the same capital reserves and might struggle to fundraise since they are unable to showcase success stories to potential investors in the current macroeconomic environment. This means they will start to pressurise their companies to consolidate, merge and create bigger organisations that will appear more capital efficient and thus have the potential for a more meaningful exit down the line.

Time to focus

Although most of the movement in this space will be motivated by necessity, there are countless advantages to M&A in the current environment. Firstly, it pushes companies to conduct vital internal evaluations to determine which assets are core to their business, allowing them to divest those they consider non-essential. This will ultimately result in a more mature company with a bolstered focus and cash to spend.

Secondly, it allows cash-rich companies to purchase spin-offs at a reduced price and go on to achieve better returns. According to PwC analysis, deals done during a downturn are often the most successful. Data from the 2001 recession, for instance, indicates those that made acquisitions had a 7% higher median shareholder return than their industry counterparts one year later.

M&A for geographical expansion

This concept will also prove useful when it comes to using M&A for geographical expansion. FinTechs that are already successful in the UK will likely look to acquire or merge with strong yet struggling competitors in other countries instead of enduring the rigmarole of setting up there from scratch. We have already seen the number of cross-border M&A announcements increase, with data from Investment Monitor’s Global FDI Annual Report 2022 showing a 45.2% jump in 2021 compared to the previous year – a trend we can expect to continue in 2023.

FinTech trends

Some of the key growth areas for M&A in the FinTech space will be Banking as a Service (BaaS) and Gen AI. As customers become increasingly dissatisfied with existing offerings, BaaS providers are rapidly gaining popularity and new players are entering the market. This is set to change, however, as regulators are beginning to force these organisations to strengthen control and their compliance functions to obtain a license-holding. Naturally, this would limit the number of new entrants in this space, making licence-holding companies extremely attractive and driving appetite for M&A or consolidation.

Gen AI can exponentially boost a company’s productivity and allow greener enterprises to disrupt big industries. Businesses already innovating in this space will become more valuable and there will no doubt be fierce competition to acquire them.

Overall, one can anticipate a flurry of M&A activity in Q3 and Q4. While not all driven by preference, companies positioned with both the financial resources and a thorough strategy will be able to capitalise on the current dubious market to make transformational deals that may contribute to their long-term success.

CategoriesAnalytics IBSi Blogs Payments

How to achieve growth and strengthen resilience using automated AR and digital payment

Marco Eeman, Managing Director, Europe, Billtrust
Marco Eeman, Managing Director, Europe, Billtrust

Times are challenging for businesses of all shapes and sizes as we enter the second half of 2023. Market volatility and slowing growth are being driven by high inflation and interest rates, economic instability, and geopolitical pressures, on a micro and macroeconomic level.

By Marco Eeman, Managing Director, Europe, Billtrust

Only resilient companies will flourish, but the IMF warned of the increased risk of a ‘hard landing’ for the global economy just last week. It predicted a 25% chance that the annual global growth rate could fall below 2% this year – double its normal level.

For businesses to rise to these challenges, companies across all industries are taking a good look at their income and expenditure. Those that will ultimately succeed recognise that it is not simply cash flow that businesses should pay attention to, it’s how that cash is flowing.

Drive growth during uncertain times

A well-executed, automated accounts receivable process can positively impact a company’s cash flow, working capital efficiency, customer relationships, risk management, and financial decision-making. By optimising this process, a company can enhance its financial stability, profitability, and long-term success, even in an extremely challenging economic climate. Digital payment systems can also deliver a series of interesting advantages.

Increased efficiency and faster cash flow

Automated AR and digital payment systems streamline financial transactions by automating processes, reducing paperwork, and minimising manual errors. This efficiency leads to cost savings and allows businesses to allocate resources more effectively, contributing to improved profitability.

Timely and efficient invoicing and collections are crucial for maintaining a healthy cash flow, which has never been more important than it is now, as it allows companies to meet their financial obligations such as paying suppliers and employees. Digital payments enable companies to receive funds quickly, accelerating their cash flow. Compared to traditional payment methods like wire transfers, digital payments are processed in real-time or with minimal delay, ensuring faster availability of funds. A robust AR solution also automates collections tasks so any overdue invoices are sorted out faster, freeing up time that can be used in more value-adding spaces.

​​Expanded customer base and global reach

Streamlining the invoicing process can help foster positive client relationships and prove reputationally beneficial. An automated approach will simplify the invoicing process and minimise errors. Also, by accepting digital payments, companies can tap into a broader customer base. Many consumers prefer the convenience and security offered by digital payment methods such as credit cards, mobile wallets, and online banking. By accommodating these preferences, businesses can attract and retain more customers, leading to increased sales and profitability.

Automated AR solutions and digital payment systems facilitate international transactions and enable businesses to expand their operations across borders. Companies can easily accept payments from customers in different countries, opening up new markets and revenue streams. This global reach enhances business resilience by diversifying customer bases and reducing dependence on specific markets.

Data insights and cost reduction

Digital and automated payment and AR systems, and the added use of AI-powered tools, generate vast amounts of transactional data which enable companies to make data-driven, risk-adjusted decisions that reflect current circumstances and offer more control during a period of significant uncertainty. By leveraging analytics and data mining techniques, companies can gain valuable insights into customer behaviour, spending patterns, and preferences. These insights can inform strategic decisions, such as targeted marketing campaigns, personalised offers, and product/service enhancements. By leveraging data, businesses can optimise their operations, tailor their offerings, and boost profitability.

Automated AR not only allows companies to optimise their way of working, but it also allows companies to save paper, printing, and postage costs and eliminate expenses associated with physical checks, cash handling, and manual reconciliation. Moreover, digital payments can automate recurring billing processes, reducing administrative overhead and improving operational efficiency.

Choosing the right solution

Businesses must focus on compatibility when looking for a modern AR provider and make sure the solution is integrated with the open Business Payment Network (BPN) and interoperable with the larger payments ecosystem. It’s also important to work with an AR partner that has an in-depth understanding of evolving payments legislation. For example, EU laws are currently changing: in December the EU published the VAT in the Digital Age (ViDA) directive which will mandate e-invoices. It’s crucial businesses implement processes that are fully compliant with all relevant trading laws and choose tech solutions that help, not hinder this.

Conclusion

Digital payments offer numerous advantages that can contribute to building resilience and driving profits for companies. By embracing digital AR systems, businesses can improve efficiency, accelerate cash flow, access a larger customer base, expand globally, enhance security, gain valuable data insights, and reduce costs,

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