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A cleaner, greener future; Why fintech can lead the way to greater sustainability

With the Intergovernmental Panel on Climate Change (IPCC) delivering a “final warning” on the climate crisis earlier this year, we’ve reached a point where green changes are needed without further delay. The urgency for investments made to be directed by sustainable hands is urgent, as we are now at “a code red for humanity.” Historically, the finance sector has always been the principal driver for incorporating lower-carbon practices, so to safeguard a green transition and avert further damage from the current environmental practice, we must look to them to lead the way.

By Jeremy Baber, CEO of Lanistar

Jeremy Baber, CEO, Lanistar

The finance sector has invested in energy-efficient and work-efficient technologies that have completely revolutionised both their own sector and beyond. The rise of fintechs, alongside investments in the use of artificial intelligence (AI), the Internet of things (IoT) and machine learning (ML) are all ways in which finance has been ahead of the mainstream in adopting newer, cleaner technology. Even the rise of blockchain that we are seeing helps proliferate cleaner technology throughout their industry and beyond.

The time for sustainable net zero or even net negative global CO2 emissions is now to ensure a sustainable future before it is too late. With environmental consciousness currently at an all-time high, both from a governmental and consumer standpoint, we need fintechs to take the helm in mediating a smooth transition towards greater sustainability. The responsibility is upon fintechs to mediate this transition to greater sustainability.

Investing in sustainability is far and wide

Fintechs have always enabled innovation and contributed positively towards sustainability for a lower-carbon world, particularly as they aim to disrupt traditional finance operations in a customer-focused way. The rise of financial technology over the past decade has created a new era of potential for sustainable investing, particularly in ESG investing, green financing, and carbon neutrality.

Digital payment solutions can lead the charge toward sustainability and a low-carbon economy. The carbon footprint brought by physical currency – i.e., its creation, transportation, disposal, etc. – is minimised or eclipsed by digital cash transactions. Utilising digital removes the need for both plastic cards and paper transactions, streamlining transaction processes in an environmentally conscious way by reducing company waste.

Consumers want a cleaner conscience

Retail Week recently reported over half of UK consumers are more likely to buy from a retailer or brand with a strong ethical and sustainable ethos, with Millennials more likely to be eco-conscious and, by contrast, the Boomer generation less so than other generations. The new market is a more sustainably-conscious one, and fintechs should be looking to capitalise upon it. Gen Z and millennials want greater transparency regarding tracking and reducing their overall impact on the environment.

Recent research from the open banking platform Tink revealed that 40% of customers wish to track impact through services provided by their bank, and therefore holding their retail businesses of choice accountable is a big part of this, especially when many will stake green claims for consumer trust but not follow through. Consumers want a clear conscience when it comes to their personal impact, and that is reflected in the retailers they choose to shop with.

Currently, there is a significant gap in the market for innovative tracking solutions, with Tink’s research suggesting a significant number of customers would switch purely for access to tools to track their carbon footprint. Whilst 30% of surveyed banks have expressed interest in offering these tools, currently, these institutions have zero plans to actually do so. Fintechs can hold feet to the fire in this regard and act in the best interests of their consumers as an intermediary for directing businesses to change. It is easy to make green claims to gain customer support, but fintech’s pushing for responsibility for the sake of their customers helps motivate action.

A business incentive is still needed

No operational change can happen without the final say of the business CEO, and no CEO will consider investing in an operation that doesn’t have some business advantage to go with it. In highlighting the rising consumer demand, business leaders can feel more secure in adopting innovative yet lower-carbon alternative technology to gain customer rapport. As customers are more discerning than ever, they are more likely to scrutinise green credentials for authenticity before committing to a provider.

By contrast, change must be meaningful and not just surface-level support. It is no longer as simple as claiming to support green initiatives; real action is needed at every step. With every initiative to attract and secure interest from target consumers, businesses must follow through or their customers will quickly seek a stronger alternative elsewhere. And whilst many bigger fintechs have greater resources to allocate to sustainable initiatives, few are actually choosing to do so.

SME fintechs have greater mobility for impactful business moves

Where financial organisations with bigger pockets may have the power to push for greener tech across the board, they seldom have the mobility to enact changes across their operations. Yet the agility of smaller fintechs to deploy sustainable initiatives has meant that they can often overtake and lead the charge for greener decisions across operations. Smaller underdog competitors suddenly have an incredible advantage with this ability to outmanoeuvre their larger counterparts limited by traditional operations.

As the fintech sector continues to mature, business initiatives can continue to refocus the ecosystem away from short-term successes and instead towards long-term green practices. But without critical support from the UK government to support and protect green initiatives, we cannot make meaningful industry change. An updated policy is needed – without the demand for profit – to best help the finance sector to put pressure on change and secure a cleaner, greener future.

CategoriesAnalytics Big Data 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.

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Five ways to empower ‘Gig Workers’ with Digital Lending Solutions

The “Gig Economy” has emerged as an increasingly relevant phenomenon in today’s job market. The work model allows professionals to offer their services independently, especially through digital channels, without being tied to traditional job roles. This method offers flexibility and autonomy, simultaneously providing the opportunity to diversify incomes and explore different areas of expertise.

By Ranjan Kumar, Head of Finance & Accounts, RupeeRedee

Ranjan Kumar, RupeeRedee
By Ranjan Kumar, Head of Finance & Accounts, RupeeRedee

With the advancement of technology and the appearance of a myriad of digital platforms, the Gig Economy has attracted a significant number of professionals. Currently, it is expanding at a CAGR of 15% and fostering a robust network of workers ranging from delivery workers, drivers, designers, programmers, and many others.

However, regardless of the nature of work, the lack of financial stability remains a fundamental constant, placing Gig workers in need of robust financial services. The burgeoning potential of this economy space demonstrates the benefits fintech can avail by tapping into this new labour paradigm, offering tailored financial solutions based on the needs of freelancers.

Why Gig workers, specifically?

Gig workers, even though they comprise 85% of India’s workforce, have irregular cash flow and limited access to financial products like credit cards or pre-approved credit lines, and any sudden expenditure can upend their stability.

Income and Wealth Management

Unlike salaried workers, gig workers are subjected to an uncertain flow of income, regular payment delays, or no employee-sponsored retirement or insurance plan to fall back on. Thus, they need to be offered financial services that systematically analyse and offer insights into their income patterns, incorporate fractional savings in their spending patterns, and provide them with education and awareness for the same.

Unique Financing solutions

Due to the unique nature of income patterns, gig workers appear as less credible than salaried workers, which leads to financial products like loans and credit cards being underserved to this segment by financial service providers. Therefore, there is a huge unmet need for hassle-free, low-interest credit, which can be given by employing tools that can assess the creditworthiness of gig workers tailored to suit the nature of Gig work.

Fintechs catering to the financial needs of freelancers

Although the Gig Economy is growing, there is still limited competition in terms of financial services, which provides a unique opportunity for fintechs to position themselves as leaders in this rapidly growing market segment. By focusing on providing tailored financial solutions like specialised bank accounts, financial management tools, and flexible lending options, they can deliver exceptional customer experience earning the trust and loyalty of gig workers.

Data Analysis and Profiling

Fintechs use leading technologies like AI and data analytics to assess credit risk in a holistic manner and gather data that allows them to understand the financial needs of this segment and provide inclusive and equitable financial services to workers in the Gig Economy.

Fintech-powered Tailored Products or Services for Gig Economy Professionals

Considering the scenario of the gig economy, new-age digital lending platforms offer low-installment-based loans that allow borrowers to not worry about immediate repayment and can, in fact, enjoy the flexibility of splitting it over a few days, weeks, or even months. Hence, they still have access to liquidity. In addition, digital lenders leverage business process management systems to automate and optimise internal processes related to the care and support of gig workers by adopting machine learning algorithms that give insight into their financial behaviour.

Furthermore, by implementing ECM systems, digital lenders can easily store, access and organise relevant information, maximising operational efficiency and ensuring data security and confidentiality of gig employees. Apart from this, in order to save money or generate a financial surplus, they offer to store money in an investment instrument at minimum rates that can be liquidated on short notice. Thus, fintech can capture an expanding market and build strong relationships with this new segment.

Future Venture

The future of the Gig Economy holds limitless potential with the development of intuitive interfaces designed specifically for the needs of gig workers. This involves offering income and expense tracking tools, providing clear reports on transactions, and providing access to relevant financial resources, which poses an incredible venture for financial service providers to attract and retain Gig Workers.

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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.

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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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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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Is regulation enough to propel Open Banking adoption?

Recently, the European Commission set out its intentions to advance open banking with the introduction of PSD3. The update to its Payment Services Directive (PSD2) shows a commitment from the EU to realise the potential of Open Banking, and it’s one welcomed by the industry.

Hans Tesselaar, executive director at BIAN

Hans Tesselaar
Hans Tesselaar, executive director at BIAN

While PSD3 sets out several key changes to realise its goal of driving Open Banking adoption forward, the aim to standardise payments across the EU with its move from a directive to a regulation poses the question: is widespread adoption possible with regulation alone?

The benefits of regulation

A new Payment Services Regulation will update and replace elements of PSD2 to ensure its rules are applied more consistently across Europe. This new regulation will bolster Open Banking by enforcing better API functionality, allowing smoother payment data sharing and eliminating unnecessary steps hindering data flow.

Apart from refining PSD2, these proposals enhance user control via a centralised dashboard, ensuring easier management of data sharing. In addition, new measures like increased bank cooperation will support the industry’s attempt to combat fraud and elevate consumer confidence.

This regulation puts FinTechs and banks on a level playing field, giving technology providers more control over the service they provide to customers through easier and more secure data sharing, while reducing infrastructure costs.

Europe is not the only country taking a regulatory approach. The UK for example, a pioneer in Open Banking innovation on a global scale, has been prioritising regulation since the launch of Open Banking in 2017 by the Competition and Markets Authority (CMA) following the introduction of PSD2. Now, its recent announcement from the Joint Regulatory Oversight Committee, regarding its commitment to a long-term regulatory framework, reaffirms its commitment in the area.

While these regulatory measures allow fintechs and banks to implement Open Banking more effectively and aim to give customers a seamless experience, independent regulation does limit innovation without the correct considerations.

The realities of regulation

The state of open banking is still very immature, but there is no denying its growth. The number of users worldwide is forecast to reach 132.2 million by 2024, a significant increase from the 24.4 million users in 2020.

Countries like the UK risk reducing their role as a driver of progress without the access to the wider European population that it had before Brexit, as an example. And as the European market is predicted to be the largest open banking market by 2024, the continent as a whole would do well to collaborate to better understand customer needs, react to market demand and expand further.

Being open to learning from global examples and listening to industry leaders, including larger banking institutions with global influence and international exposure, will be important to ensure successful practices are promoted, which will encourage open banking more widely within these countries and different regulatory frameworks.

Meeting the demand

Focusing on regulation must not overshadow market demand, and looking at countries with a market-driven approach, such as Singapore, will reveal what governments and organisations should be prioritising when it comes to open banking.

Singapore’s market-driven stance has led to high open banking adoption. 90% of professionals consider open banking either a ‘must have’ or ‘important’ and a further 90% agree that it has also had a positive impact on the industry and made it more collaborative. This is despite no mandatory requirements.

Adoption has accelerated in APAC over the past few years due to the opportunity it has to make the industry more collaborative and the potential to bring about fairer and more equal financial services. However, the space remains in the early stages of development. Many banks are just starting their digital transformation journeys, and struggling with core legacy systems and closed or outdated architectures. This is why overcoming these barriers and industry collaboration will be at the heart of open banking adoption.

Coreless banking

Regardless of a regulatory or market-driven approach to open banking, banks must create an ecosystem with fintechs, providers and aggregators. This is to boost the speed at which best-of-breed products can be implemented to meet customer demand and make the most of the opportunity that lies within the open banking space.

A coreless banking solution will be key to empowering banks to overcome issues around interoperability and selecting the software vendors needed to obtain these best-in-class solutions for each application. In turn, this will promote industry collaboration and ensure customers are provided with the optimum service to further encourage open banking adoption.

Coreless banking implies that each of the needed (IT)-services works seamlessly together. If this is established, financial institutions can migrate to a “best of breed” environment so they will have the ability to utilize and combine third-party solutions to deliver the best open banking services for their customers.

This means banks can focus on incorporating the technology they need to enable open banking services and respond to customer demand – regardless of whether this is from a regulatory or market-driven starting point – at a faster and more efficient pace.

The answer lies in collaboration.

Is regulation enough for open banking adoption? The short answer is no.

Whether countries decide to push open banking from a regulatory standpoint, or adoption is driven from the market demand, industry collaboration will be the answer. This will enable greater innovation, so from PSD3 in Europe, to Singapore’s market demand, the industry can unlock the ultimate outcome for open banking with an open attitude.

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Bridging the Gap: the crucial role of last mile data integration in financial services

Financial firms worldwide are striving to achieve last mile data integration, a process that seamlessly integrates data into business workflows and puts it at the disposal of business users. The goal is to eliminate the need to search through databases or data warehouses for required data, allowing easy access for reporting and financial models, and enabling better decision-making.

By Martijn Groot, VP Marketing and Strategy, Alveo

By Martijn Groot, VP Marketing and Strategy, Alveo
By Martijn Groot, VP Marketing and Strategy, Alveo

Financial services firms spend material amounts on acquiring and warehousing data sets from enterprise data providers, ESG data companies, rating agencies and index data businesses.

However, when this data is not readily available to business users or applications where it impacts decisions those investments will not deliver the return they should be. For many financial services businesses, last mile data integration represents a missing link in ensuring they are optimising the value they obtain from data. The volume of data they need is continuously growing and the bills they face for acquiring it are therefore going up in tandem.

Activating data assets

Ultimately, firms will not get the best out of their investment in data, if they don’t have a way, first, to verify it, and second, to land it into the hands of their users or enable users to self-serve. If the data is conversely, still sitting in a database that is hard to get to, or needs skills to access, then the business will not achieve maximum value from it.

That in a nutshell is why last mile data integration is so important to them. Achieving it does however come with challenges.  Organisations must establish efficient data onboarding processes and transform data sets to meet diverse technical requirements common in their applications landscape. Additionally, maintaining high service levels and responsiveness to requests for new data to be onboarded is vital to build trust and keep business users engaged.

So how can all this best be achieved? The key is efficient data management. To use an analogy, financial data management can be seen in the context of the human body, with the need to manage data flows analogous with the circulation of blood through the arteries. Data gushes in from internal and external sources.

It needs to be cleaned and a process of data derivation and quality measurement applied and then we see the end result in the form of validated and approved data sets.  The overall flow often stops at that point for financial services organisations. But such an approach is incomplete in that it actually ignores last mile data integration. Data may be flowing through the arteries of the organisation but it is not reaching the veins, and capillaries.

That’s where the key step of distribution comes in. This not only enables easier access to the data in whatever format required by lines of business within the organisation but also to set up exports or extracts of relevant data in predefined views or formats that then flow easily into business applications.

Maximizing data ROI

Financial sector organisations understand the need to do this but often they end up doing it in a way that involves a lot of ad hoc manual maintenance at the individual desktop level, which means that process get out of sync; data becomes stale and there is the danger of duplication. All this inevitably ends up impacting the quality of decision-making also.

Effective last mile data integration is an automated process that involves identifying relevant data sources, mapping and cleaning the data and then transforming and loading it into the target system and using data quality and consumption information in a feedback loop. The key to this process is making it easy for the specific business user. It is about understanding the kinds of taxonomies and nomenclature the user is expecting and then being able to mould, build and shape the data being presented in a way that best suits that user.

Financial services firms that get all this right will be well placed to unlock the full potential of their investment in data and maximise the ROI on the data they purchase. Ultimately, by delivering on this process and verifying and making data readily available to users, organisations will put themselves in the best possible position to make informed decisions, streamline operations, and position themselves for ongoing success.

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The rise of eCash: Why more consumers are using cash online

Megan Megan Oxman, Interim President Digital Wallets at Paysafe
Megan Oxman, Interim President Digital Wallets at Paysafe

Here’s how using cash online helps customers take control as they manage online security concerns and economic uncertainty. According to our Lost in Transaction 2023 payment trends research report, the number of consumers using cash online is growing, and quickly.

By Megan Oxman, Interim President Digital Wallets at Paysafe

In fact, eCash, which enables consumers to generate a barcode and pay offline at a conveniently-located store, is more popular than at any other point since our Lost In Transaction research series began in 2017.

Now, 31% of people who used cash online in the previous year are paying with it more often than they did 12 months ago. So what’s driving this growth, and how can businesses help customers embrace this increasingly popular payment method?

How popular is eCash?

Overall, 30% of respondents who used eCash in the past year say it’s their preferred way to pay online. This makes it the fifth most popular online payment method after debit cards, credit cards, digital wallets such as Skrill or NETELLER, and credit cards stored in Apple Pay, Google Pay or a similar mobile wallet.

And many of those who use cash online, rely upon it: 23% of respondents who say eCash is their preferred payment method would abandon their cart if they can’t pay with it. Put simply: businesses may lose custom if they don’t provide this payment method.

Why are consumers using cash online?

The greatest drivers in the growth of using cash online are the rising cost-of-living and concerns about online security. In times of economic uncertainty, consumers often turn to cash to help them exercise more control on their spending and stick to a budget.

With eCash becoming more widely available as purse-strings tighten, it’s no surprise that consumers would take the opportunity to take this money-saving technique digital. Now, more consumers are using cash online to control online spending just as they use physical cash to manage their outlay in stores.

It’s telling that, while eCash usage has increased across the board, the spike has been greatest among respondents who changed payment habits due to the cost-of-living crisis, with 60% using eCash more often. The budgetary benefits of using cash online are not to be underestimated.

eCash offers a secure online payment alternative

Our research also found consumers view eCash as a more secure online payment option, particularly when it comes to online video gaming and iGaming. Almost half (49%) of respondents who pay for online gaming told us cash-based methods are the safest way to make online purchases.

As to why this is the case, eCash payments don’t require consumers to share any financial details online — a key concern about ecommerce, with 52% of respondents explaining that they don’t feel comfortable sharing financial details online.

eCash and the bottom line

Businesses should be looking to incorporate a number of different payment methods to cater for consumers’ needs, and eCash can be fundamental to satisfying both budgetary concerns and settling nerves around security.

The right payment platform can enable businesses to do this – helping to meet customers’ needs by allowing them to take control of their spending and their personal data.

To learn more about why a growing number of consumers are using cash online, check out our Lost in Transaction 2023 report.

As to why this is the case, eCash payments don’t require consumers to share any financial details online — a key concern about ecommerce, with 52% of respondents explaining that they don’t feel comfortable sharing financial details online.

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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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