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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 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.

CategoriesAnalytics IBSi Flagship Offerings

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.

CategoriesAnalytics IBSi Flagship Offerings

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.

CategoriesAnalytics IBSi Flagship Offerings

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 Cybersecurity IBSi Flagship Offerings

Chargeback fraud is growing – can AI and Big Data stem the tide?

Monica Eaton, Founder of Chargebacks911
Monica Eaton, Founder of Chargebacks911

According to our research, 60% of all chargeback claims will be fraudulent in 2023. This means not just that merchants have to consider that chargebacks claims are more likely to be fraudulent than legitimate, but that individual merchants and the anti-fraud industry need to lay the groundwork to collect and analyze data that will show them what fraud looks like in real-time.

By Monica Eaton, Founder of Chargebacks911

While many industries are benefiting from so-called ‘big data’ – the automated collection and analysis of very large amounts of information – chargebacks face a problem. The information that is given to merchants concerning their chargeback claims tends to be very limited, being based on response codes from card schemes (‘Reason 30: Services Not Provided or Merchandise Not Received’), meaning that merchants would have to do a great deal of manual work to reconcile the information that the card schemes supply with the information that they have on hand.

While Visa’s Order Insight, Mastercard’s Consumer Clarity, and the use of chargeback alerts have reduced the number of chargebacks, merchants still have very little data on chargeback attempts. This article will look at how merchants can improve the level of data they receive on chargebacks and how they can use this data to create actionable insights on how to improve their handling of chargebacks.

What is big data?

2023’s big tech story is undoubtedly AI – specifically generative AI. Big data refers to the large and complex data sets that are generated by various sources, including social media, internet searches, sensors, and mobile devices. The data is typically so large and complex that it cannot be processed and analyzed using traditional data processing methods.

In recent years, big data has become a crucial tool for businesses and organizations looking to gain insights into customer behavior, improve decision-making, and enhance operational efficiency. To process and analyze big data, companies are increasingly turning to advanced technologies like artificial intelligence (AI) and machine learning.

One example of a company that is using big data to drive innovation is ChatGPT, a large language model trained by OpenAI. ChatGPT uses big data to learn and understand language patterns, enabling it to engage in natural language conversations with users. To train ChatGPT, OpenAI used a large and diverse data set of text, including books, websites, and social media posts. The data set included over 40 gigabytes of text, which was processed using advanced machine-learning algorithms to create a language model with over 175 billion parameters.

By using big data to train ChatGPT, OpenAI was able to create a language model that is more accurate and effective at understanding and generating responses than previous models. This has enabled ChatGPT to be used in a wide range of applications, including customer service chatbots, language translation services, and virtual assistants. Currently, technology very similar to ChatGPT is being used by Bing to replace traditional web searches, with mixed results, but, like self-driving cars, it is a matter of ‘when’, not ‘if’ this technology will become widespread.

AI and fraud

Chargeback fraud is a growing problem for businesses of all sizes. The National Retail Federation estimates that retailers lose $50 billion annually to fraud, with chargeback fraud making up a significant portion of that total. With the significant rise of online shopping, this type of fraud has become even more prevalent, as it is much easier for fraudsters to make purchases using stolen credit card information, forcing victims of fraud to then dispute the charges with their credit card issuer.

Chargeback fraud occurs when a customer disputes a valid charge made on their credit card, claiming that they did not make the purchase or that the merchandise they received was not as described. If the dispute is upheld, the merchant is forced to refund the money to the customer, along with any associated costs, and is typically charged a penalty fee by their payment processor. This not only results in a financial loss for the merchant but can also damage their reputation and lead to increased scrutiny from payment processors.

Where can machine-learning technology help with fraud? To understand this, we have to first understand its limitations. ChatGPT and Large Language Models (LLMs) like it are not Artificial General Intelligence (AGI) – the sci-fi trope of a thinking computer like HAL 9000. Although they can pass the Turing Test, they do so not by thinking about the given information and answering accordingly, but by matching what looks like an appropriate answer from existing text.

This means that while they can produce perfect text by copying existing text rather than ‘thinking’ about the substance of the question, they are prone to producing errors. This is something that isn’t acceptable when it comes to fields like fraud prevention – nonsense answers with a veneer of truth won’t work in the binary world of whether a particular transaction was fraudulent and unfounded accusations of fraud can damage a merchant’s reputation.

What is needed then are AI solutions built specifically for chargebacks. Companies like Chargebacks911 have been working on this for years now, and their solutions are based on big data models that have been built up over that time. Because of their extensive experience working in that field, they are the ideal partner to work with to bring AI up to speed and address the problem of chargebacks.

CategoriesAnalytics

The economic downturn will see greater innovation in FinTech: Three tips to thrive

Hannah FitzsimonsIt’s no secret that FinTech businesses have been fighting an uncertain economic environment over recent years. Landslide economic challenges have put every British business under extreme pressure, but our industry has shown its resilience. It’s the ability to adapt. To evolve. And ultimately, to continue to thrive despite uncertainty.

Hannah Fitzsimons, CEO, Cashflows

In fact, according to the latest CBS Insights report, FinTech companies are still thriving in the marketplace. And bigger businesses are taking note of the industry’s strength. Take Apple and its high-yield savings account for example. The company is actively seeking to increase and establish its fintech presence – and I wouldn’t be surprised if we see other Big Tech companies follow suit.

Why is FinTech maintaining its resilience?

People will always need to spend money, and with online payments being the second most common payment method in the UK, the opportunity for FinTechs is huge. Consider Buy Now Pay Later (BNPL); before the pandemic, BNPL was a term that many consumers likely hadn’t heard of, with a transaction value of just £34 million globally. In 2023, it’s predicted to reach a global transaction value of £300 million – a more than ten-fold increase – supporting consumers to access the products they love in a way that works for their financial situation.

Amongst wider economic challenges, fintechs need to continue this evolution. To consider the needs and wants of British consumers and design and deploy services that do not just meet but exceed expectations. In my experience, diamonds are made under pressure, and FinTech businesses need to harness this opportunity to not only survive but thrive.

Navigating the storm: Why strong leadership is essential

Strong leadership is essential to fostering innovation, especially in challenging economic times. Leaders must be able to navigate uncertainty, quickly identify emerging trends and be able to pivot strategies to stay ahead of the curve. To be able to execute this requires a strong, creative team. People are the most important part of a business, and as such, need to be supported through challenging times by business leaders.

To foster a culture of innovation where every employee feels valued, heard, and appreciated, FinTech leaders need to inspire their employees. They must be bought into the company’s innovation journey and feel passionate about its success.

The leaders who establish these relationships and build agility into the business from the top down can not only weather economic downturns but emerge from them stronger and more innovative than ever before.

The power of understanding consumers

In my opinion, innovation needs to make a real difference to the end user. Whether that’s giving a SMB rapid access to its business payments, or providing real-time spending behavior insights, the ultimate innovation measurement is the end impact.

However, before we can get to impact, businesses first need to identify the opportunity: understanding consumer behaviour and spending trends.

For example, at Cashflows, we’re always looking to innovate in line with our customers’ needs. To understand those needs, we surveyed small and medium businesses to understand their hesitations about switching payment providers. The research found that of the businesses that had switched merchant acquirers in the past, two in five experienced frustrations during the process. Companies cited challenges such as needing to submit significant amounts of documentation (61%) and having to share the same information multiple times (54%).

Using this insight, we created AI-powered fast onboarding to streamline merchant onboarding. Listening to customers influenced our decision-making and in turn, allowed us to create and invest in an innovation that would yield the greatest impact for not only our customers but our business.

From Insight to action: Creating and delivering a winning strategy

In business, you’ll hear how important a well-crafted strategy is almost every other day. Yet, many businesses are still yet to put a truly cohesive strategy in place. With the economic downturn changing customer behaviors and market conditions evolving rapidly, I think every business should have a comprehensive strategy to guide their product roadmap and effectively communicate a route through tough times.

When looking at innovation, particularly in an uncertain economic climate, a sound strategy will help FinTech day-to-day to adapt to changes and prioritize investments in initiatives that align with the company’s long-term goals and missions. In hard economic times, it’s easy to get lost in the day to day running of the business, fighting fires as they arise. However, by investing the time to develop a comprehensive strategy, FinTech businesses can boost productivity, stay ahead of the curve, and emerge stronger from economic downturns.

The key to success is the strategy execution. The strategy plays a crucial role in establishing the business’s direction; however, the execution of that strategy is what brings tangible changes throughout the company. This is where the workforce comes into play. To effectively implement a strategy, it is vital to engage employees and align them with the business’s vision and objectives. By fostering a culture of engagement between employees and the company, the organization will thrive, especially during challenging times.

Strong leaders, customer understanding, and a clear strategy. The points seem so simple yet foster huge opportunities for fintech businesses battling the economic downturn. We’ve already shown the amazing impact fintech innovations can have on supporting people and businesses through times of hardship. By taking stock and prioritizing strategic decision-making, the fintech industry will continue to thrive. I’m excited to see the next innovation that revolutionizes spending.

CategoriesAnalytics Artificial Intelligence IBSi Flagship Offerings Loans

Specified user FinTechs are helping lenders ride the AI wave for origination and underwriting

Raman Vig and Sudipta K Ghosh co-founders of Roopya
Raman Vig and Sudipta K Ghosh co-founders of Roopya

The Indian digital lending industry is undergoing a major transformation due to its unprecedented pace of growth. As per the recent stats – more than 200m people have availed of retail loans in a year and this is growing at 20% CAGR.

By Raman Vig and Sudipta K Ghosh co-founders of Roopya

The significant rise in the disbursement volume not only exhibits the uptick in the number of borrowers but also demonstrates the emergence of digital lending players in the market.

Many FinTech companies are overshadowing brick-and-mortar lending institutions by digitising every aspect of the lending process. This can be attributed to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) models that expedite and enhance the lending process. Given the scenario, the new-age lenders are moving from traditional risk models to a data-backed approach to be more relevant in the market.

A major step towards addressing gaps in the lending ecosystem

Data is the most critical element for any AI / ML model. In lending, credit bureau data and alternate data becomes the base for any propensity model for loan origination, preparing scorecards for underwriting, or even creating early warning signals on existing portfolio.

Hence data becomes the most powerful and significant force that drives the digital lending industry. In the present ambiguous scenario, the Indian lending industry has flagged several concerns on the dynamics of the data distribution of borrowers among lenders.

India has more than 1200 active lenders, out of which, only 1% have access to advanced data and analytics tools. This creates a significant gap on the supply side as small and mid-sized lenders lose out on the data-driven lending race. The new-age loan origination and underwriting tools which are accessible only to large-sized lenders create a huge disparity in data intelligence. Consequently, these lenders have to incur high acquisition and underwriting costs, ultimately leading to high-interest rates for borrowers.

Grappling with an unregulated lending scenario, the Reserve Bank of India (RBI) planned to put a guardrail on the ecosystem. The apex bank announced the appointment of a new set of FinTech companies as ‘Specified Users’ of Credit Information Companies (CICs) under the Credit Information Companies (Amendment) Regulations Act, 2021 based on stringent eligibility criteria. These Specified User FinTechs get access to credit data, run analytics and help digital lenders make data-driven decisions.

The appointment of Specified User FinTech players has not only regulated credit data distribution but also resulted in more streamlined and secure digital loan processing.

AI underwriting models

Every year, over 15 million ‘New to Credit’ borrowers enter the credit ecosystem. This makes loan underwriting a tricky process for lenders under the existing conventional models. Every customer or borrower has unique financial circumstances which bring uncertainty many inches closer to making credit decisions.

If an underwriting practice is not backed by data and analytics, it can lead to economic meltdowns for lenders. And that’s where Specified User FinTechs come to the rescue, providing lenders with the ability to interpret enormous data amounts much faster and more accurately than conventional underwriting practices. It equips lenders with AI and ML-backed underwriting models, adding an extra layer of better oversight on how data sets can be used strategically to come up with personalized solutions for each borrower.

FinTech players are one of the early adopters of technology. The advent of Specified User FinTechs helped lenders to venture into segments that were deemed high-risk by conventional lenders. Simply put, they have been successful in bridging the accessibility gap for underserved lenders, making them ride the wave of AI.

Predictive algorithm to streamline the lending process

In practical terms, AI works intuitively like predicting defaulted or paid loans. Specified User FinTech combines AI algorithms with ML classification mechanisms to create probability models for lenders to have better credit decision ability. The technologies are applied to improve credit approval, and risk analysis and measure the borrowers’ creditworthiness, which further helps small and mid-sized lenders scale with ease.

FinTech companies that are recognized as Specified Users have competencies to store huge amounts of credit data and build AI and ML models on structured and unstructured data sets. This provides more streamlined and better insights for borrower segmentation, predicting loan repayment, and helping in building better collection strategies. Besides this, Specified User FinTechs are helping lenders to be on top of automation whether in loan underwriting or pricing for personalized offerings.

On a similar backdrop, lenders’ ability to recognize early warning signs proves to be highly beneficial for lenders with credit risk management. Recognized by RBI, lenders can be certain of the credibility of Specified User FinTechs in terms of data and analytics.

Specified User FinTechs leverage the intuitive yet data-backed behavior that detects any suspicious borrower and red flags as fraud. Unlike traditional tools of analysis, it can alleviate the possibility of human errors arising from biases, discrimination, or exhaustive processing practices. By utilizing NLP (Natural Language Processing), lenders can accurately generate warning signals instantly.

Final Thoughts

The landscape of digital lending in India is continuing to evolve. Lenders can reap the benefits of data hygiene performed by AI and ML infrastructure established at the Specified User FinTech’s end. By automating and bringing all significant practices to one place, lenders are empowered to improve customer experience, take leverage of predictive analysis, enhance risk assessment, and improve credit decisions and breakthrough sales bottlenecks.

CategoriesAnalytics venture capital

Surviving and Thriving: How Indian FinTech start-ups can insulate against funding winter

Rahul Tandon, Chief Product Officer, Safexpay
Rahul Tandon, Chief Product Officer, Safexpay

A funding winter is a period of reduced venture capital funding during which investors become cautious and risk-averse, resulting in a lack of funds for new businesses. The global economic meltdown has had some knock-off effect on the Indian FinTech industry as well. But the rate of adoption of Indian FinTech is still rising and shining. As per the Economic Survey 2022-23, Indian FinTech companies witnessed a staggering adoption rate of 87% across various sects of users including the underserved and those who belong to the bottom most stratum.

By Rahul Tandon, Chief Product Officer, Safexpay

This beats the global average by 23%. With over 2100+ FinTech companies, India is the third-largest FinTech ecosystem in the world. Despite the challenges, Indian FinTech start-ups attracted investments worth $1.2 billion in Q1 2023, a sharp jump of 126% compared with $523 million raised in Q4 of 2022, according to a report compiled by market intelligence platform Tracxn.

However, the total funds raised were 55% lower than $2.6 billion raised in Q1 2022. The number of funding rounds in Q1 2023 also experienced a drop of 77% and 39% against Q4 2022 and Q1 2022, respectively. The ecosystem has remained resilient, promoting innovation, improving operational efficiency, and prioritising regulatory compliance to succeed.

FinTechs Modifying Business Model

In the Indian financial services industry, partnerships have played a vital role in sustaining operations and generating cash flow. To adapt, businesses have adjusted their models, forming alliances and collaborations. FinTech companies often collaborate with banks, NBFCs, and insurance firms, leveraging their customer base and accessing resources. Such collaborations enable them to expand their offerings, such as digital lending platforms and payment solutions. FinTechs have also taken steps to conserve cash by scaling back on activities like marketing, prioritising cost-effective approaches. By aligning expenses with revenue streams, start-ups aim for sustainable growth and attracting investor interest.

Innovation is not only in products and services but also in business models. The reason being that entrepreneurs often get funding in a 12-18 month period, those who have not secured consecutive rounds of funding may have a limited runway. As a result, it is critical for FinTechs to run a business, which is sustainable and open to adaptation. Overspending on client acquisition and other unnecessary areas could be fatal for the growth and sustenance of the business. Focus should be on improving unit economics and being conservative with the initial funding. Start-ups, especially in FinTech, can boost their prospects of long-term success by implementing these actions.

Fostering Innovation

Innovation has been a driving force for Indian FinTech start-ups to attract investors and differentiate themselves in a highly competitive landscape. These start-ups have embraced cutting-edge technologies and developed innovative solutions to address the evolving needs of consumers. For instance, they have leveraged artificial intelligence, machine learning, and blockchain to create secure and efficient financial services platforms.

Government support has played a crucial role in fostering a culture of innovation and securing funding during challenging times. The Indian government has introduced initiatives like the “Digital India” campaign and the “Start-up India” program, which provide support and incentives for FinTech start-ups. Such government initiatives have encouraged entrepreneurs to develop innovative solutions, attract investors, and contribute to the growth of the FinTech ecosystem. Furthermore, ongoing innovations such as differentiated banking and insurance licenses, the introduction of Central Bank Digital Currency (CBDC), the implementation of Account Aggregator, the emergence of the Open Credit Enablement Network (OCEN), the integration of Digilocker, and the establishment of the Open Network for Digital Commerce (ONDC) are fuelling continuous progress in the sector.

Enhancing Operational Efficiency

Indian FinTech startups recognise the importance of optimising their operations to save money and exhibit profitability potential. Leveraging technology to increase operational efficiency is a key strategy for fintech companies. By automating manual processes, implementing artificial intelligence and machine learning algorithms, and utilizing big data analytics, FinTech firms can streamline their operations and reduce costs. For example, digital on boarding processes can significantly reduce the time it takes to open an account or process a money transfer. Additionally, chatbots can provide customer service around the clock, freeing up staff time for more complex tasks. These innovations not only lower operational expenses but also improve consumer experience, attracting a wider user base.

Credibility and Regulatory Compliance

FinTech and payment companies in India face a complex and evolving regulatory environment. Compliance requirements include obtaining licenses, adhering to data protection rules, complying with AML and KYC regulations, ensuring secure technology infrastructure, maintaining accurate records, submitting reports to regulators, and undergoing audits.

For FinTech start-ups to receive finance, trust and regulatory compliance are critical. They realise the need of preserving clients’ data, employing effective security measures, and adhering to relevant regulations. With data breaches and privacy concerns on the rise, start-ups have prioritised data security measure while maintaining transparency and responsibility in their operations.

Furthermore, forging solid alliances with banks, financial institutions, and regulatory agencies boosts the legitimacy of the whole ecosystem. Collaborative efforts to build regulatory frameworks, encourage responsible lending practises, and defend consumer interests foster a trust and confidence ecosystem.

The future of regulatory compliance in Indian FinTech and payments looks promising with the government’s push towards digitisation and financial inclusion. The apex bank has been working towards creating a more robust regulatory framework to ensure that the growing FinTech industry remains compliant with regulations. One of the key initiatives taken by RBI is the creation of a regulatory sandbox, which allows FinTech companies to test their products in a controlled environment before launching them in the market.

Way forward

The future of Indian FinTech industry is in position for growth and resilience, overcoming the challenges posed by the funding winter. To attract investor interest, FinTech companies should adapt their business models, forge strategic partnerships, and prioritise sustainable growth. Innovation will remain a crucial factor in setting them apart from competitors, with a focus on building scalable and profitable enterprises while optimising operational efficiency through technology integration.

Upholding credibility and regulatory compliance become paramount, encompassing data security, transparency, and responsible practices. By collaborating with banks, financial institutions, and regulatory bodies, FinTech firms can foster a reliable ecosystem. With government support and regulatory initiatives, the future looks promising for the Indian FinTech and payments industry, as it continues to drive financial inclusion and digital transformation across the nation.

CategoriesAnalytics Artificial Intelligence

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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