The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

The FinTech industry has witnessed a massive shift owing to digital transformation. From banks to e-commerce platforms, astronomical amounts of data are being generated in the form of transactional and non-transactional data.

Ruled by the power of algorithms and data science, it is enabling businesses to spot consumer trends, and empowering them to create real-time growth opportunities. In a fiercely competitive environment like the payments industry, data science approaches have already matured.

Despite the industry being highly regulated, businesses can attain an edge over their competition by leveraging powerful insights unearthed through data science. The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

Here are the top three ways in which data science is being leveraged by the FinTech industry:

1. Fraud detection and prevention– The number of frauds, as well as their new mechanisms, make it difficult for traditional rule-based approaches to detect them. A scalable way to keep track of fraud is to use data science. Data science techniques are widely utilized to identify and predict fraudulent financial transactions. Gradient boosting models are a popular choice. If interpretability is an important factor, more simple models like logistic regression could be used, or advanced techniques like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanation (SHAP) could be tapped to explain more complex models. Owing to an exponential rise in the number of daily online transactions, there is a need for FinTech players to place fraud prevention on the top of their agenda. Employing the right mix of predictive analysis, behavioral profiling, and real-time detection, data science can enable financial organizations to keep abreast of new ways of committing fraud with low to no manual intervention in an automated fashion using algorithmic approaches. While fraud detection and prevention are critical aspects that data science can aid, their true potential and capability extend far beyond these functions.
2. Credit scoring models- Assigning a credit score to people who quantify the likelihood of default is an extremely important part of FinTech companies dealing with providing loans. In some emerging economies, people prefer not to have bank accounts, leading to discrepancies in accounting transactional details holistically. This has posed a significant challenge to the FinTech industry to assign them a credit score. Businesses are harnessing the power of data science techniques like profiling based on psychographic surveys to go beyond the traditional credit scoring methods which require a banking history. From geocoding, analyzing SMS messages to psychographic surveys, these data points could serve as a substitute for traditional banking history and might predict likely defaulters. Technologies like machine learning are playing a key role in providing loans to people who are not yet in the formal banking sector.
3. Customer lifetime value models- To grow more, businesses need to sell more, which can be achieved by acquiring new customers. A recent Gartner survey revealed that 44% of CMOs expect marketing budgets to decrease because of COVID-19[i]. This will mean an increased focus on ensuring that customer acquisition costs (CAC) are reduced. With the dynamics of business changing rapidly and revolving around its customers, it is very critical to get to know a customer’s lifetime value (CLV). CLV enables businesses to concentrate their efforts on their best clients. Better their understanding of CLV, the better they can employ their strategies to retain their most profitable customers. Another efficient way to apply this would be to use machine learning models to calculate customer lifetime value (CLTV models). The CLTV can ensure that customers identical to existing customers with a higher CAC than their CLTV are not acquired again.

Today consumers have numerous payment methods at their disposal, there is not a single value-based ecosystem that effectively connects cash, digital, and loyalty rewards today. The FinTech Industry is enormous in its own right, and by employing the advanced methods offered by data science it can scale hitherto unknown heights of growth and profit.

Herein lies a crucial opportunity for businesses to drive engagement, higher customer satisfaction, and elevated experiences.

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There is no doubt that artificial intelligence (AI) and machine learning (ML) is becoming a hot topic within the fintech industry. At almost every seminar and conference, we are hearing about the rise of these emerging technologies and the potential they have to disrupt businesses.

It’s clear that AI and ML is a blueprint within which the fintech industry is operating. However, what is apparent is that no matter how much fintechs bang the drum of the impact of AI on enterprises, it still remains underutilized by many companies due to their inability to visualize, integrate and adopt these new technologies.

Recently, there has been a great deal of conversation across multiple industries around the potential of these technologies, but according to research by Accenture, 87 percent of business leaders in the UK are struggling with how best to adopt it. 

That’s not to say that there isn’t an understanding of its importance for enabling strategic priorities. Indeed, three out of four C-Suite executives believe that if they don’t scale AI in the next five years, they risk going out of business completely.

Nonetheless, there remains a gap between ‘hype’ and ‘practical implementation.’ Less than 5 percent of companies have successfully

industrialized AI, while 80 percent to 85 percent are pursuing discrete proof of concept products – where the power of AI and machine learning is disconnected from business outcomes or strategic imperatives. Many companies do not sufficiently tap into the full potential of emerging technologies, consequently limiting their business impact.

With its expansive historical and structured data, fintech is a fertile ground for artificial intelligence and machine learning technologies to generate bespoke products and solutions, to help businesses increase profitability and save costs.  So, why are companies generally slow to adopt, implement, and scale emerging technologies in their short, mid-term and long-term strategies?

Embracing the benefits of AI and ML

Many companies are slow to adopt AI and machine learning due to a lack of technical know-how – from both an integration point of view, and a limited understanding of its value to their business.

It is essential that companies work with the right people to commission AI and ML products and solutions that have tangible business benefits and impact at the customer level.

As a former Silicon Valley technologist and research engineer for a major technology company, I have found that these technologies can play a vital role in operations across a business. Companies can identify opportunities where cost savings can be made, while simultaneously increasing efficiencies, making it easier for the CFO to embrace their role as a key contributor to the growth of the company.

By using a combination of AI and machine learning technologies, businesses can identify opportunities that companies are missing to accelerate their day-to-day activities and processes. These technologies enable customers to make smarter decisions and operate more effectively. Meanwhile, emerging technologies will increase growth opportunities to aid business development across the globe, helping companies to thrive in an international environment.

According to recent research, executives weren’t struggling to scale AI because of budgetary constraints, but rather the operational challenges of integrating these technologies into their current business processes. The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.

It is precisely these aspects that differentiate companies that have successfully scaled AI and ML, versus their counterparts pursuing siloed proof of concepts. Not only should company bosses move towards adopting AI and ML as part of their go-to-market business strategies, but they should also actively work towards integrating and encouraging the adoption of these technologies into their day-to-day operations.

Unlocking data insights

The beauty of AI and machine learning lies in its ability to unlock data insights not previously accessible by traditional manual processes. It is also business size agnostic, i.e. the scaling success rate or the return on investment for using AI and ML is not determined by a company’s size. Instead, it’s more important to focus on implementing the right AI and ML capabilities and mindset in your organization’s company culture. Whether you’re a start-up, scale-up, or a large corporation, AI and ML can be used to fuel your company’s growth strategy.

The business advantages of strategically scaling emerging technologies are vast; these companies achieve nearly twice the success rate and triple the return on their AI investments rather than those companies pursuing siloed projects.

Successful case studies of leveraging AI and ML are far and wide: a Japanese life insurance firm which used AI to calculate payouts to policyholders saw a 30 percent increase in productivity and savings of around $1 million a year. Similarly, one AI-powered underwriting platform has enabled auto lenders to cut losses by 23 percent annually and more accurately predicted risk. An AI-powered cybersecurity company has been used by top US banks to distinguish between real customers and bots; its machine learning model enabled one major bank to protect its customers from account hijacking and in its first week of use, detect one million ‘credential stuffing’ attacks. So, not only can AI and ML boost profitability and save costs, but it can also safeguard your company from fraud and security breaches in the future.

Less talking and more doing

For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation.

As an industry, we need to talk less and start doing more, to embrace the business impact that AI and machine learning bring. These technologies should no longer be seen as a clip-on solution; they are integral – now in the present moment – to every business model. It is important to formulate a plan and integration strategy of how your business will use artificial intelligence and machine learning, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.

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The Israel Securities Authority (ISA) and the Israel Innovation Authority (IIA) launched a new program this week for fintech startups aimed at offering direct collaboration between regulators, regulated entities, and companies in the industry.

Called Data Sandbox, the program supports Israeli companies that meet “the real-world needs of the industry,” the Innovation Authority said. It is run in collaboration with the TASE (Tel Aviv Stock Exchange) with the support of the Israeli Ministry of Finance.

The Data Sandbox initiative will offer a direct interface between regulators, regulated entities, and high-growth fintech companies. Companies participating in the project could gain access to the databases of the ISA and the TASE.

Five Israeli companies were selected for the program which will run for approximately six months, according to the announcement. They offer solutions in areas such as liquidity provision, digital tools for verification and authentication, identification of trading anomalies, and tools for portfolio investment managers.

The solutions will help meet the challenges faced by the Securities Authority and the capital market, such as bolstering Stock Exchange liquidity, improving compliance processes while reducing affiliated costs, and making data accessible for regulated entities, the statement said.

The companies selected for the program include Zirra.co, a Tel Aviv-based trading analytics firm that offers clear and structured data-driven insights through artificial intelligence — built to determine risk of investment; Scanovate, a Ramat-Gan based firm that will establish an SaaS-based Identity Provider on a cloud for clients of Israeli investment houses; and Correlate Capital, a tech company specializing in the development of trading and liquidity provision systems that created a system that provides liquidity and other products.

Two companies were selected to conduct a pilot with the authority while using trading data received directly from the TASE. Fintica AI will use artificial intelligence and Wizsoft (Israel), which provides enterprise resource planning (ERP) solutions and operates machine learning and data mining systems, will run algorithms for the pilot.

“The goal of this pilot program with the Israel Securities Authority is to allow local high-tech companies to implement their technology for the benefit of the Tel Aviv Stock Exchange and capital market, thus improving the market-readiness and market penetration of their products while also providing solutions to the challenges faced by the Israeli economy,’ said Aharon Aharon, CEO of the Israel Innovation Authority.

“The path to achieving this goal lies primarily in sharing the know-how and data accumulated by the Authority along with additional regulatory entities with Fintech companies which offer technological solutions for the capital market, and to guide the pilot process while providing solutions for regulatory issues,” adds Anat Guetta, chairperson of Israel Securities Authority.

Aharon said that because there is a high demand for the program and the variety of solutions that were submitted, thus both the ISA and the IIA have decided to launch an additional call for proposals, scheduled to close later this year.

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While it is a cliché to observe that data is the gold of the 21st century, few have considered the role of processing in this metaphor: raw data must be processed to become precious.

At the scale of most businesses, the volume of data is too great for humans to handle, so we must turn to data science.

This is especially true in today’s current situation as more people turn to technology to stay connected and keep their businesses afloat. Big data processing allows companies to complete complex tasks like risk assessment, providing financial access to groups of people who were previously inaccessible.

Nascent big data technologies such as machine learning have already been applied in fintech.

At Channel VAS, we apply big data processing techniques to our micro- and nano-finance solutions and enable lenders to provide credit to the underbanked at greatly reduced costs.

Such techniques are still in their infancy, but as they continue to advance over the coming decade they will further empower fintech firms to serve new customers, especially in the developing world, and leave an indelible mark on the global financial landscape.

Big data as a key enabler of financial services innovation

Big data has revolutionised value generation for the financial services industry.

Providers constantly strive to innovate and improve their tools, services and offerings to enhance customer loyalty and surpass their competitors.

In this struggle, big data and machine learning are key. They allow fintech companies to complete the typically protracted and expensive tasks of credit risk scoring and assessments faster and more affordably.

Emerging markets are the primary beneficiaries, as they seldom have an established credit registry.

These technologies are also able to process mobile phone usage and payments data more effectively, in order to help lenders in emerging markets understand credit risks.

Similarly, the ability to produce new credit risk models for nano- and micro-finance benefits the underbanked by providing a broader range of options and access to the financial highway.

The continued evolution of big data

Big data’s utility will grow concurrent with the evolution of the Internet of Things (IoT), progressing mobile technology, and more advanced authentication techniques.

Fintech companies will, therefore, continue to focus on the accumulation and processing of data by actively investing in data science departments. For instance, at Channel VAS, we rely on big data to develop the proprietary analytic tools and credit scoring algorithms which form the foundation of our business.

Such developments have generated new lending possibilities for previously underbanked and underserved audiences.

Data science and fintech are joined-at-the-hip, and together they will overturn the traditional approach to doing business before the decade is out.

This will be particularly apparent in early fraud detection and preventive security, where collection and analysis of data – rapidly and accurately – will provide unprecedented safety.

Similarly, comprehensive customer profiles, customer segmentation, personalized financial offerings, and process automation are other areas that big data will transform in the 2020s.

Overcoming regulatory hurdles

For big data to truly realise its immense potential, however, a drastic shift in the regulatory framework is required. Regulators still seem stuck in an outdated mentality that prevents them from unlocking big data’s true possibilities.

The pandemic has revealed the importance of a robust digital financial system, particularly in emerging markets.

To make this the decade where the unbanked move online, regulators must develop a centralised database by consolidating and centralising data from other regulatory parties such as financial institutions, insurers, telephone companies, aggregators, and payment services.

Such a centralised database must not only be agile and enabling, but also committed to mitigating risk and maintaining the safety and security of all stakeholders.

Fintech companies could then access the aggregated data with full customer consent to provide them with services.

Hopefully when we emerge from pandemic lockdown we will have a greater appreciation for the digital tools that we have relied upon, and regulators will realise that digital alternatives to conventional services are feasible after all.

All indications are that these services will become more pervasive, seamless and customer-focused over the next decade.

Therefore, regulators, banks, and mobile network operators need to join hands with fintech providers to fully utilize big data for the ultimate benefit of the customer, in the years to come.

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Data science, which involves developing methods of recording, storing and analyzing data to extract useful information and gain insights, has created a drastic change in the financial services industry.

Considered as the core of fintech, data science and its allies in the form of artificial intelligence (AI), machine learning (ML) and big data, have brought the sector to the next level, with now applications in areas including risk evaluation, fraud detection, payments and transaction records, as well as asset management.

In risk evaluation, data science allows fintech companies to assemble faster and more accurate credit evaluation processes; in fraud prevention, it helps enhance detection and prevention processes through real-time monitoring and evaluation; and in asset management, data science is enabling fintechs to crunch huge amounts of data and construct asset management models.

But besides the ability to provide customers with more personalized products and services, evaluate credit risk more rapidly and precisely, and fight threat and fraud in real-time, data and analytics also allow financial services firms to take a much more holistic view of how their businesses are performing, providing them with more complete insights to support strategic decision making.

The International Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics solutions to reach US$274.3 billion by 2022, growing at a five-year compound annual growth rate (CAGR) of 13.2%. This growth will be driven by digital transformation and rising demand for better, faster, and more comprehensive access to data and related analytics and insights, said Dan Vesset, group vice president for analytics and information management at IDC.

Banking and Big Data

The industries currently making the largest investments in big data and business analytics solutions are banking, discrete manufacturing, professional services, process manufacturing, and federal/central government, according to IDC.

Given its potential to simplify financial decision making and enable superior, personalized solutions, data science has become a critical component in incumbent financial institutions’ digital transformation strategy, with executives in financial services, as well as in technology, media and telecommunications, citing AI and big data as two of the top five most impactful technologies in financial services, alongside the Internet-of-Things (IoT), 5G, and cloud computing, according to PwC’s 2019 Global Fintech Survey.

The trend is also reflected in hiring trends with major global banks including Citi, UBS and Morgan Stanley, all beginning to ramp up their data and intelligence capabilities in 2018, racing to hire data analysts, data scientists, data platform mangers, and more, according to a research by data intelligence platform Outside Insight.

With data science rapidly becoming a prerequisite for any finance company looking to maintain a competitive advantage, Refinitiv, a global provider of financial market data and infrastructure, has introduced Refinitiv Learn-It-All-Labs, a series of hands-on, interactive sessions aimed at helping equip the financial services data science community​ with the latest skills and ML use-cases in bite size 30 minute sessions.

For its first session, called NLP for Capital Markets 101, Refinitiv explored the impact of natural language processing (NLP) and deep learning on financial organizations and the overall industry, and the different techniques the company uses to make sense of its unstructured data. The session also included a live demo of SentiMine, a new Refinitiv Labs project which applies NLP to portfolio management.

Refinitiv’s next Learn-It-All-Labs virtual lab session will take place on May 21, at 8am BST, and will focus on design thinking. In this session, the company will share its design thinking skills and explain how it has applied them to a real-life project called the Data Access Tool.

Key takeaways will include a deep dive into what design thinking actually means, the difference between traditional thinking and design thinking, how to apply a design thinking to a project, and more.

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Zaggle had a turnover of INR 1800 Cr in FY 2018-19 and aims to target INR 3500 Cr in FY 2019-2020.

The company has successfully provided innovative and disruptive fintech solutions to over 3500+ Corporates Clients and has Merchant Partner Relationship with over 8500 + merchants and has 3.5 million users. Their eminent list of clients includes Microsoft, Vodafone, Samsung, Zydus, Star Health Insurance, Hiranandani and many others.

Talking to Express Computer, Raj N Phani, founder and Chairman, tells us about his voyage ahead.

How do you think is the Fintech sector is going to upsurge in the near future?

The digital payments industry will give a big boost to the economy because Partners, FinTech Players and Merchants are urging people to go cashless. Banks have also been asked to use disinfectants on cash notes while dealing with transactions. The impact on payment apps is not yet clear as the country’s financial system is already reeling under the burden of Yes Bank’s collapse. On the whole, caution and a wait-a-watch approach are clouding the mood within the world of start-ups.

What is the exclusiveness of your startup, that’s unique from other gigantic players in the market?

This is Demonetization 2.0 for Digital Process Transformation and Zaggle will play a Crucial Role since Employee Reimbursement and Expense Management Automation & Digitization will Save upto 80% cost. Companies will have to ensure overall Manual Process Automation to rationalize the cost since digital solutions are cost-saving and efficient.

Zaggle has been winning new client contracts in spite of the sales teams and clients working from home because companies have now realized how important is it to quickly digitize processes to save cost and be efficient. Zaggle sees 100% growth in business in the next 9 to 12 months and it will only increase since the digital adoption will be the new mantra from most of the companies.

What is the latest mode of technology that you are catering to?

Zaggle is an award-winning Fintech company digitizing spends to unlock value and drive business growth. Zaggle uses Deep Tech and Artificial Intelligence to provide platform solutions to businesses for expense management & employee reimbursements as well as for Rewards & Recognition.

Zaggle deploys cutting edge technologies AI (Artificial Intelligence), ML (Machine Learning) and OCR (Optical Character Recognition) in providing seamless experience to businesses and users.

How important do you think is it for people to rely on technology? Are there any major follies per se?

Today is the era of technology, you are nobody without technology, Online Presence is a compulsion for running any business. Technology has improved our lives and the present things are now better, faster, easier and more convenient and everyone is getting used to it. The birth technology has brought Fintech or the era of digitalization that we are living up to.

Coming to the financial perspective, are you a bootstrapped venture? If not, kindly elucidate on the nature and amount of funding raised.

Yes, we are a funded company.

What are your immediate and long-term milestones like?

Short term milestone would be managing losses with speed and adequacy where we have to do cut staff cost if required,

Zaggle will be hiring 100 New Employees in the next 12 months because we see this as an opportunity to hire good talent since there are mass layoffs happening around. We are going to see a minimum of 100% growth in the next 12 months because CXO’s are realizing the Value of Process Digitization and Zaggle being an enabler will see the maximum growth.

Lastly, any words of advice for the wantrepreneurs?

So, looking at the current situation the wait could be long so there isn’t any right or better time it is the right time to start the venture but cut the budget and ensure that if you wanted to start today you should have started yesterday. Make sure your venture can create an impact on the economy, use this time to reach out to the clients to help them save more and do more and more business.

Launch before you feel ready. If you wait until your product or service feels perfect, someone else will already be doing a better job of helping your customers solve their problems. Validate your business idea by launching fast, bringing on a small group of paying customers and adapting to make your solution great for them over time. The right availability of Technology, capital investment, emergence of government policies and an entrepreneurial and innovative mind-set are the ones to grow.

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Just like the famous Gold Rush of 1849, nowadays businesses are dipping their toe in the data mine, in order to seek some value out of it. This huge chunk of data is forcing the fintech and the banking industry to unleash the power of the hidden gems that data analytics can deliver.

One can’t deny the fact that banks and financial institutions generate astronomical amounts of data in the form of customer transactional and non-transactional data. Reports state that 2.5 quintillion bytes of data are being generated every day. Have we ever thought whether this data is a promise or a peril? It is no surprise that conventional data-processing fails in managing this large volume of data and provides insights that are far from reality.

Realising the value of big data requires an analytical eye and technologies such as big data analytics, AI, and machine learning. These help in churning down data into meaningful information, thus minimising the risk decisions based on intuition.

That’s where the role of data scientist comes into the picture. A data scientist has mastered this treasure hunt as it requires them to know exactly what information to look for that will act as a booster in cross-selling and customer satisfaction. In the banking industry, a data scientist can help develop customer profiles, predict behaviours and track trends, to name a few.

According to a survey, the banking and financial services sector is the biggest market for analytics and data science professionals with 44 per cent of all jobs created in this domain. This percentage will grow in the coming years as this sector is actively using data to derive business insights and improve scalability.

The emerging role of data scientists

Over the past few years, the banking industry has achieved new heights through innovative means for evolving customer expectations of personalisation and convenience.

Earlier banks and other financial institutions used to follow a one-size-fits-all strategy where every customer was treated with the same approach irrespective of their needs and interest.

Gone are those days when customers would visit banks for every single service like depositing, checking account balance, etc. Customers now use their mobile phones to check their account balances, deposit checks, pay bills, and transfer money.

According to a research commissioned by Relay42, the data management platform (DMP), “Digital banking is growing in popularity with 53 per cent of consumers using or willing to move to an online or mobile only bank — 27 per cent have moved already, while 26 per cent are considering the switch”.

There was a time when it would take a few years to build a framework that helps banks in gathering an overall picture of their customers. Since online banking is gaining popularity, adopting big data analytics becomes all the more important. Thus, all this has given room to the new and ever-growing career of the data scientist. A data scientist helps provide meaning to the raw data and uses it to draw insights for better analysis. They help banks in establishing a 360-degree approach for their customers by the analysis of:

* Customer spending patterns

* Customer segmentation

* Implement risk management processes

* Customised product offerings

* Customer loyalty

In addition to this, data scientists help banks in designing, building and maintaining the complex data flows, tools and solutions that are needed from the bank’s data systems to analytics environments.

Moreover, data scientists tend to toil away in advanced analysis tools such as R, SPSS, Hadoop, and advanced statistical modeling. Thus, easing the process of generating valuable information from the piles of data and provide inside based on key metrics with suggestive best practices.

It can be rightly said that the fintech domain has benefited from the emergence of analytics.

The path forward

It’s high time that banks adopt big data analytics to remain relevant and profitable in this hyper-competitive business environment. Experts like data scientists will be an edge to these growing trends and will bolster the future of the fintech industry.

This career has never-ending benefits and it poses a promising future for the data science space as data has become the new oil to drive decision-making. One of the biggest challenges faced by the modern banking industry is legacy systems that aren’t equipped to handle the big data revolution. So, banks will need to align their people, processes, and technology platforms to provide highly personalised customer experience by extracting insights from data.

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