‍

This listicle is adapted from our latest series on A Beginner’s Guide to EmFi. To learn more about the different categories of EmFi used across industries and understand the main barriers to implementing this new model, check out the full article here. 

‍

Embedded finance (EmFi) has emerged as a major trend that may transform financial services. By disrupting the traditional financial industry, EmFi offers an innovative approach by integrating financial services into non-financial products or services using enablers such as Banking-as-a-Service (BaaS) or Application Programming Interfaces (APIs). Thus, it provides businesses with access to sensitive financial information and gains customer insights. This creates new opportunities for businesses to provide financial services that are more convenient and accessible for customers, making transactions seamless and efficient. 

‍

According to a report by Dealroom, EmFi can open up opportunities beyond the combined value of all fintech startups and top world banks and insurers. It is also expected to grow its worth to more than $7 trillion by 2030. As more consumers adopt digital payments, digital-first business models emerge, along with APIs and open banking, which leads to the industry significantly growing over the next few years.

‍

EmFi comes with potential risks, as with any new technology and increased convenience. These include customer and security risks and the potential for fraud. In order to address these challenges, businesses such as credolab have developed solutions to mitigate risk, detect fraud, and optimise marketing spend. Credolab's behavioural data analytics platform uses machine learning (ML) to identify behavioural patterns and calculate insights from smartphone and web metadata.

‍

This article will explore how businesses can level up with credolab solutions in the four categories of EmFi: embedded banking, payments, insurance, and investments. 

‍

1. Embedded Banking

‍

Embedded banking provides a new way of integration allowing non-financial businesses to offer their users banking products like a savings account or a branded card, such as linked accounts and debit or credit cards. For example, integrating traditional banking services like checking accounts and debit cards with non-financial businesses like retailers. With the rise of digital and online transactions, the mobility of money has also increased, especially with embedded banking at play. 

‍

Challenges for Embedded Banking

‍

The shift to a digital landscape has increased the difficulty of avoiding scams or stopping potential fraudulent transactions. Although advantageous to customers and businesses, the ease of digital and online transactions also benefits fraudsters who can now exploit the vulnerabilities of Banking-as-a-Service (BaaS) solutions and gain access to sensitive financial information. BaaS solutions offer the duality of financial and non-financial services, such as lending licences to other clients. In contrast to a digital version of the bank, BaaS allows banks to expand their services and open new revenue streams.

‍

However, BaaS has its drawbacks, including its difficulty in implementing as banks have to de-couple their legacy banking system and turn it into a fully digital, composable or modular technology stack or build an entirely new one. This overhaul leads to the challenge of predicting whether an applicant will commit fraud. The best way to tackle this is by adopting a proactive approach, especially since first-party fraud detection determines the likelihood of a payment default during the loan application process. This is where credolab's solutions for early fraud detection using ML algorithms come into play.

‍

How to level up with credolab

‍

Credolab offers solutions for early fraud detection using ML algorithms, leading to the seamless reduction of first-party and third-party fraud at the onboarding stage. These ML algorithms identify behavioural patterns from about 70,000 privacy-consented and permissioned data points collected and transformed into over 10 million behavioural features. As a result, tools such as device velocity checks, IP address velocity checks, and fraud scoring can be turned into potential red flags able to detect first-party fraud, data manipulation, and device and behavioural anomalies.

‍

Furthermore, credolab leverages ML to process large data sets and create models to find anomalous patterns between user behaviour and the likelihood of suspicious actions. Using signals built on top of the smartphone and web metadata makes it possible to collect and then convert raw data into granular customer insights. These insights, created in conjunction with TransUnion’s TruValidate product, will help flag potential scammers for each applicant in a secure, non-intrusive, frictionless, and real-time manner.

‍

As a global partner of the TruValidate product, credolab offers a one-stop-shop solution for combating fraud and assessing risk during onboarding. Businesses can make more informed decisions by combining credolab's checks and scores with internal checks. They can even automatically blacklist or reject IDs once they have been confirmed fraud cases using analysed data from ML algorithms detecting fraudulent signals or alarms.

‍

Key Benefits

Making better fraud decisions

Credolab's ML algorithms help discover behavioural patterns likely to be linked to suspicious or fraudulent behaviours. In conjunction with TransUnion's TruValidate product, these algorithms enable more informed fraud decisions to be made by transforming signals into rich insights.

‍

Some of these signals used include:

• IP mismatch
• Suspicious device behaviour flags
• Early fraud flags
• Score to predict loyalty points abuse
• Score to predict cashback abuse
• IP address velocity
• Atypical application behaviour flags
• Device velocity
• Same ID, different devices fraud
• Fraud score
• Same device, multiple ID fraud

‍

‍

Mitigating fraud risks

The above-identified signals and flags can be turned into granular insights for businesses to assess and mitigate potential fraud risks. 

‍

As addressed in A Beginner’s Guide to EmFi: Part 3 How to Leverage credolab’s Solutions,  these identified red flags are combined with user-level, granular insights and complemented with:

• A comprehensive database of confirmed fraudulent devices
• The ability to identify device and account linkages
• A more secure method without using personal data
• An agile and adaptable rule configurator

‍

‍

Using credolab's solutions, businesses can level up their fraud detection and strategy to achieve optimal results, such as up to 23.9% reduced fraud costs and up to 21% decreased first payment default. In addition, with the rise of embedded finance, credolab's solutions provide a proactive approach to detecting fraud, especially in embedded banking, before it happens, available to all businesses, including non-lenders and non-financial institutions. 

‍

2. Embedded Lending 

‍

Embedded lending is a concept that is gaining traction in the financial industry. It is a growing trend involving integrating loan and instalment payments into non-financial products and services to improve loan approvals' accuracy. In the absence of credit histories, assessing risks and maintaining approval accuracy can be difficult, and it becomes a challenge for traditional lenders. This is because credit histories were essential for lenders to accurately determine a borrower's risk level. Consequently, traditional risk assessments tend to exclude those who are unbanked or underserved and who are more likely to receive loan rejection. This method leaves out many people who could benefit from financial services and only contributes to widening financial exclusion.

‍

Challenges for Embedded Lending

‍

One of the most common examples of embedded lending is Buy Now, Pay Later (BNPL) services. According to a report from the Consumer Financial Protection Bureau (CFPB), the number of BNPL loans issued to consumers in the United States (US) grew from 16.8 million in 2019 to 180 million in 2021. Furthermore, BNPL's borrower base consists primarily of younger borrowers and leans more toward Millennials than Gen Z. They provide more favourable lending options as these services are often available to those with no credit history, low credit scores, or access to traditional financial services. This alternative option helps improve customer risk assessment and, as a result, opens doors to new customers. However, customers, merchants, and providers of BNPLs face risks such as being unable to repay the full amount, which can damage their finances and reputations. 

‍

Proper security strategies, such as rule-based risk assessment and real-time data enrichment, must be implemented to overcome these challenges and ensure all parties' safety.

‍

How to level up with credolab

‍

Credolab offers solutions that improve credit risk assessment, even for those without credit bureau histories. Going beyond a rule-based risk assessment, credolab's behavioural data analytics platform leverages ML algorithms to identify behavioural patterns from smartphone and web metadata, such as using a single device, browser or IP address for repetitive loan applications and analysing the frequency of applicants changing key information such as income or address. These behaviours are converted into granular insights to assess risk and predict the probability of early default. After all, this assessment is the first step in building an underwriting and fraud strategy that leverages multiple data sources for any embedded lender, especially BNPL providers.

By implementing a robust risk detection strategy, credolab's risk solution can help businesses reduce non-payment risk, prevent fraud, and improve customer experience and satisfaction. Credolab's insights, such as device, browser, or IP address usage, combined with BNPL providers' existing (and, at times, rather traditional) strategies, can determine a borrower's risk level and legitimacy. This is done by processing raw behavioural data into scores that identify the correlation with micro-behavioural patterns similar to applicants who are identified as fraudulent or risky. Furthermore, the emergence of new data sources using ML and Artificial Intelligence (AI) algorithms allows traditional and alternative data to create more predictive scoring models.

‍

With its unique source of smartphone behavioural metadata, credolab is also a trusted partner of TransUnion, which has integrated the credoSDK into the TransUnion Digital Onboarding solution, tailored specifically for BNPL customers. Combining these two technologies provides a seamless, end-to-end credit risk assessment, fraud assessment, compliance assessment, identity verification service, and payment facilitation across multiple touchpoints powered by the power of device data.

‍

Key Benefits

Streamlining Application Process

Any customer's first experience with a financial institution sets the tone for their overall satisfaction. In most cases, the first encounter occurs during the onboarding process. With TransUnion's Digital Onboarding and credolab's solutions, financial institutions can simplify their application process, minimise the need for multiple integrations, and conserve time and resources for IT teams. 

‍

With a secure and multi-functional API system, this solution enables an easy signup process, including ID document scanning, OCR, and data verification against TransUnion's extensive identity data sets. As a result, the application form is pre-filled to prevent human error and enhance the user experience.

‍

Enhancing Identity Verification

Identity verification ensures that the person applying for a loan or opening a bank account is whom they claim to be. Customer identity verification refers to the validation process of an individual's information and identity.‍

TransUnion enhances identity verification by evaluating the applicant's consumer identity with background checks on the applicant's consumer data such as consumer email, phone, device SIM swaps, number portability, anti-money laundering screenings and geolocation checks. Furthermore, using Truvalidate's identity verification for identity insights also helps detect fraudulent or stolen identities through a robust view of identity risk, real-time fraud alerts, and contextual alerts for SSN verification on top of consumer data. 

As a result, the applicant's consumer identity can be securely verified, and identity fraud can be simultaneously assessed to expose any fraud risks with the gathered identity insights.

‍

Improving risk evaluation

Transunion evaluates consumer risk transparently in the background with its thorough identity verification. By integrating CredoSDK into TransUnion's digital onboarding solution, lenders can conduct alternative credit checks alongside affordability checks and avoid risky lending procedures. 

The easy-to-use application is finalised by verifying all information and bringing consumers one step closer to their purchase. Ultimately, overall risk evaluation is improved as credoSDK work in collaboration with TransUnion's solution to assess risk and reduce fraud risk with the following rules:

• Watch-list
• Anomaly
• Risk profiles
• Device Risk
• Evidence
• Geolocation
• Velocity
• Credoscore

‍

‍

Securing Biometric Authentication

Digital identity verification expands the concept of identity verification to today's remote world using processes such as biometric authentication, facial recognition and digital identity document verification. As a result, companies, governments, and financial institutions can securely and seamlessly verify an individual's identity using these digital verifications.

‍

After verification, customers can pay the first instalment using their credit cards or bank accounts. Once the account is completed, future access is seamless using biometric authentication, which refers to verifying the authenticity of a person, where it compares the applicant's individual characteristics to their biometric "template". This authentication further reduces fraud risks like account takeover fraud by using unique physical characteristics such as fingerprints or face IDs to verify one's identity. Furthermore, this will increase friction-right consumer engagement, resulting in higher conversion rates and reduced fraud and credit risks.

‍

Using credolab's solutions, businesses can take their results to the next level to generate an alternative risk score for all mobile and web applicants and achieve optimal results, such as up to 21.9% reduced risk costs and up to 32% increased approval rates. Moreover, as embedded lending enables access to new customers and offers more favourable lending options, credolab's solutions provide a proactive approach to assessing borrowers' risk levels for non-lenders and non-financial institutions.

‍

3. Embedded Payments

Finding a credit card and entering its information can lead to customers disregarding a digital purchase if they do not have their card on hand. With embedded payments, individuals can save their preferred payment method for future online purchases. 

‍

On the other hand, businesses are beginning to see the benefits of embedding these processes across the whole value chain and controlling the entire revenue stream. The embedded payment process facilitates checkout, optimises time, improves customer experiences, and reduces the risk of cart abandonment. Fast embedded payment is already available in many apps, especially delivery and transportation apps.

Challenges for Embedded Payments

‍

It is, however, a top target for fraudsters, causing a growing concern for businesses and consumers alike. According to the latest estimates, criminal fraud involving payment cards will cost $49.32 billion out of the total payment card volume of $79 trillion by 2030. 

‍

One of the most common forms of fraud affecting businesses worldwide is chargeback fraud, also known as friendly fraud, which occurs when people falsely deny using their credit or debit cards in order to receive a refund. Transactions using cryptocurrencies like bitcoin are not subject to chargebacks but are still vulnerable to other types of fraud, such as transaction fraud. This occurs when stolen payment cards or data are used for unauthorised transactions. In crypto, fraudsters use tactics like investment schemes where they impersonate businesses or government agencies and convince people to buy cryptocurrency, which they then steal for investment. Account takeover fraud is another tactic where fraudsters steal private information by intercepting Wi-Fi signals in public locations. In 2022, hundreds of user account on Crypto.com were hacked, leading to stolen funds.

How to level up with credolab

‍

According to a Federal Trade Commission report, cryptocurrency scammers have stolen over $1 billion since 2021. Thankfully, chargebacks and transaction fraud can be stopped in real-time. Alternative data makes it possible to identify legitimate customers and suspicious activity in real-time, minimising fraud by analysing users' login behaviours.‍ This applies to all embedded payment options, from credit cards to crypto.