#FinancialAPIs

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In-Depth Study of Embedded Finance Market Dynamics: Evaluating Market Size, Share, Growth Forecast

The global embedded finance market size is anticipated to reach USD 588.49 billion by 2030, expanding at a CAGR of 32.8% from 2024 to 2030, according to a new report by Grand View Research, Inc. The increasing integration of financial services into non-financial platforms, creating seamless and personalized experiences for consumers, is a significant factor contributing to the growth of the market. This convergence of industries, commonly referred to as "banking-as-a-service," allows businesses outside the traditional financial sector to embed financial products directly into their offerings.

Furthermore, the rise of Application Programming Interfaces (APIs) is transforming how financial services are accessed and consumed. The accessibility and interoperability provided by APIs enable businesses to integrate diverse financial functionalities effortlessly, thereby contributing to the growth of the market.

Advancements in technologies like artificial intelligence and machine learning are propelling the development of sophisticated algorithms for risk assessment, fraud detection, and customer personalization, thereby enhancing the overall efficiency and security of embedded financial solutions. As the Embedded Finance ecosystem continues to evolve, collaboration between fintech innovators, traditional financial institutions, and various industries will play a pivotal role in driving innovation and expanding the scope of embedded financial services.

Governments across the globe have given high importance to fostering financial inclusion, especially in developing economies characterized by substantial unbanked and underbanked populations. With the rapid increase in internet and smartphone adoption, several nations aim to emphasize the shift toward a cashless economy while ensuring that it doesn't compromise the delivery of convenient and accessible financial services to their residents. Embedded finance emerges as a pivotal tool in simultaneously addressing both of these objectives.

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Embedded Finance Market Report Highlights

Based on type, the embedded payment segment dominated the market in 2023. The growth of the segment can be attributed to the growth in adoption of digital payment across the globe

Based on business model, the B2B segment dominated the market in 2023. The B2B embedded finance provides businesses with new revenue streams, helping them improve their financial performance

Based on end-use, the retail segment dominated the market in 2023. The growth of the segment can be attributed to the rising demand for seamless and integrated financial solutions within retail ecosystems

North America dominated the regional market in 2023. The growth can be attributed to the increasing collaboration between fintech firms, traditional financial institutions, and diverse industries. This collaborative ecosystem facilitates the embedding of financial services seamlessly into various non-financial platforms, enhancing customer experiences. Additionally, the rising prevalence of digital wallets, contactless payments, and mobile banking services underscores a growing consumer preference for convenient and tech-enabled financial solutions

In February 2023, Transcard Payments announced a partnership with Coforge Limited, a global digital services and solutions provider. Under this partnership, as a Transcard Payments value-added reseller, Coforge Limited leveraged Transcard Payments’s suite of embedded payment solutions

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A New and Innovative Credit Scoring Model for Non-Traditional Business Lenders

  Applying old-school credit scoring methods and using out-of-date data where new market conditions prevail could restrict lending and hinder economic growth. For such lenders of this type, being shy away from technology and resistant to change could jeopardize opportunities to grow loan portfolios and boost profit.

Lenders traditionally rely on credit scoring based on historical financial data that don’t reveal a business’s ability to repay its loan amount. In this approach, lenders request a mile-long list of personal and business documents from potential borrowers. The financial data includes credit history, annual revenue, balance sheet, income tax returns, profit & loss statement, collateral, and other historical financial statements of the borrowers. 

In this credit scoring model, financial data has more weight in determining how stable and efficient the borrower is when it comes to funding their business. Lenders start by determining the ability of a borrower to repay its debt obligations with interest in a given period. For the most part, this typical credit scoring relies on the tenure of the existing loan, the amount and loan type a borrower already has, and current interest rates on the loan. 

Lenders use these reports to evaluate the risk profile of the borrower, which helps lenders determine whether to approve a loan or not. Obviously, a borrower with an excellent credit score tends to get a low-interest loan than those with a poor credit score, though the final decision is made by the lender, with FICO inputs used as guardrails. 

No doubt, the pandemic has eroded the credit scoring of many businesses who have closed or shut down or lost their biggest clients/customers, entailing innovative ways to assess borrower creditworthiness.

Low-Quality Data and The Unbanked

For example, an excellent credit score does not guarantee that a business will generate the same revenue and will be consistent for its payments in the future. Moreover, it won’t tell if a business has shut down or is struggling with cash flow issues. A new and innovative solution to this increasingly widespread problem is leveraging forward-looking data for determining creditworthiness. 

One of the most robust, accurate, and real-time data sources of information is cash flow predictive data. The perfect blend of accounting, banking, financial, and cash flow data help lenders see the past, present, and future financial health of a business and make data-informed intelligent decisions. 

Today, some futuristic financial technology companies empower non-traditional business lenders to examine borrowers’ banking, financial, accounting, and predictive cash flow data to take note of spending habits and monitor future income and expenses. 

Some advanced lending-tech firms enable lenders to access and analyze these data sets from the click of a button. With the help of artificial intelligence and machine learning, these FinTech firms are creating a single financial data APIs for lenders to collect, review and analyze the most accurate and efficient data of their borrowers. 

By leveraging this valuable data, lenders can accurately predict the future financial health of a business, and ultimately reduce loan bias, provide customized solutions, enhance customer experience, lessen credit risk, determine creditworthiness, and take their business forward. 

In the current lending scenario, non-traditional lenders must adopt new and innovative credit scoring methods for businesses that are unbanked or underbanked. 

Both Forward-Looking and Historical Data are Vital

New credit scoring is an augmentation and not a replacement of traditional credit scoring. Forward-looking data are more numerous and less organized than the data used in traditional credit scores. 

Fortunately, besides historical data, real-time financial data is now available for lenders, and state-of-the-art technologies are helping lenders to access, analyze and normalize this financial data. Using this data, lenders can understand their customers better, create new and innovative financial products, and build better-performing loan portfolios. 

Today, FinTechs can see a business’ past, present, and future data, enhanced by AI and ML, which means benefits for both customers and lenders. So, if you’re a non-traditional business lender who is struggling to determine the creditworthiness of your borrowers, this new credit scoring technology is your perfect solution. By utilizing a financial data API and using it for credit scoring, you can position yourself as an industry leader and become the financial expert of the future.

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Importance of Cash Flow Predictive Data During Crisis

COVID-19 outbreak has severely affected businesses across the world, especially small businesses that were temporarily closed during the pandemic. Even most small business entrepreneurs are still struggling for profitability. 

Today, many entrepreneurs are looking for working capital to keep their businesses afloat. But, unfortunately, traditional banks aren’t set up to meet them. Late payments can increase the risk for banks trying to decide who to extend credit to, and without that data, banks are struggling to figure out whether a small business has closed, low cash reserves, unstable cash flow, or is behind on their existing debt. 

Banks Reluctant to Lend Money To Small Businesses

If you’re a small business and need working capital to run and grow your business, you will likely not get money through a traditional route. 

Since small businesses don’t have a steady income, it’s harder for small businesses to be approved for new credit. Even if you’re a business that makes really good revenue - there’s no equivalent for you. Simply because you don’t have a steady income, it’s harder for you to be approved for new credit. 

The Problem Lies in the Traditional Credit Analysis

The biggest problem is that small businesses or startups, even those who make good revenue and profit and have an excellent credit score, look like risky applicants for banks.

Banks want to be confident that the business they’re providing loans to will be able to pay it back. Most banks commonly use credit score, revenue, business history, and collateral to quantify and decide whether an applicant is eligible for credit. But they ignore cash flow which is a much more accurate measure for a small business.

Technology such as artificial intelligence and machine learning makes it possible for traditional banks and other financial institutions to use cash flow predictive data to predict the financial future of a small business, faster. Cash flow predictive data can include past, present, and future accounting, banking, and other financial data of a borrower. Critically, it includes cash flow data supported by numerous forward-looking data points which is an important factor in small business underwriting.

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API Use Cases That Will Transform the Financial Lending Industry

Cash Flow Data is The Way Forward 

As the Coronavirus outbreak has negatively impacted small businesses, revenue and credit score are no true guarantee that a small business will be able to repay the borrowed amount, principal plus interest. As a lender, it’s a great way to access all the important information about your borrowers to make data-driven and informed lending decisions. 

Cash flow predictive data has transformed the banking and financial sector and will continue to rise in the future. Today, many financial institutions across the world are able to access cash flow predictive data with the help of financial single APIs. There are a lot of FinTech firms that are creating ground-breaking APIs using ML and AI that lenders can adopt to determine creditworthiness, reduce credit and make better decisions. 

Cash flow predictive data helps fill in what's missing from traditional underwriting data to make lending more inclusive. Moreover, it can also help lenders extend credit to small businesses that have just started their new venture, or have limited or no credit history - which will be particularly relevant in the wake of the Coronavirus.

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Working capital: It is the funds required to manage day-to-day operational activities.

Working capital management process: It is the process of managing short-term assets such as cash receivables, inventory, or marketable securities.

Working capital operating cycle: It is the duration in which raw material, finished goods, work in progress, debtors get converted into cash.

The four key components of working capital management are:

- Cash Management

- Inventory control

- Account receivables

- Account Payables

Methods to improve working capital

Select appropriate KPIs to measure: The vital parameters for working capital management are; debt to equity ratio, operating cashflows, outstanding days payable (DPO) to monitor the success of accounts payable, accounts receivable turnover ( DSO), and outstanding inventory days ( DIO) to control inventory turnover.

Minimize inventory and improve turnover ratio: Avoid stockpiling, holding less of slow-moving inventory, and rising the turnaround period of inventory can help in achieving high working capital.

- Inventory management and evaluating inventory performance metrics are ways to optimize inventory. Also, methods like, JIT strategies, lean inventory manufacturing avoids stockpile ups.

- Ratios, such as days inventory turnover (DIO) helps in determining the average number of days a firm holds its stock. This ratio provides a better understanding of inventory turnover.

Moving to electronic payables and receivables:

Replacing manual activities with automation is another way of optimizing the working capital process. This can help in reducing errors, provide reminders for payment to collect/pay, risk of loss of invoices can be mitigated.

With the help of financial API, the turnaround process of payments gets minimized as day-to-day transactions notification can help BFSI in ranking the priority of making/collecting payments.

Reference: ForwardAI