This April, The Fintech Times is focusing on all things embedded finance, the integration of financial services into non-financial products and services. As the space rapidly develops, we look to highlight the latest developments, initiatives and challenges embedded finance has to offer and overcome across the globe.
Artificial Intelligence (AI) has dominated discussion across all fintech sub-sectors for at least the last couple of years and continues to do so. With this in mind, it only makes sense that we delve into the impact that AI has had on the embedded finance space as part of our April monthly theme.
Enhancing safety and efficiency
Kicking us off, Mikhail Dunaev, chief AI officer at Comply Control, a UK company specialising in technology solutions for banks, explains the size of the impact AI is having on embedded finance: “Artificial intelligence has substantially influenced the field of embedded financial services, enhancing safety and efficiency.
“By analysing big data and rapidly assessing risks, AI empowers financial companies to make well-informed decisions. However, a significant revolution lies ahead – the personalisation of services based on individual user assessments. AI will analyse the unique needs and preferences of customers, offering customised solutions. This trend will foster the development of more customer-centric financial products, enhancing user loyalty and satisfaction.”
Improving decision-making processes
Natasa Kyprianidou, senior director at management consulting firm Alvarez & Marsal, also looks deeper into AI’s transformative impact: “AI has significantly transformed the embedded finance sector by enhancing decision-making processes, particularly in credit assessments and risk management.
“Traditional credit decision timelines, extending over weeks or months, have been dramatically shortened to seconds thanks to AI-driven algorithms. These algorithms analyse extensive data sets to accurately evaluate creditworthiness, making financial services more accessible and responsive to consumer needs.
“AI’s contribution extends to intelligent underwriting, where it enables the creation of sophisticated risk profiles by analysing a wide range of data, including non-traditional indicators that might be overlooked in manual processes. This approach not only fine-tunes the assessment of credit risks but also customises financial products to meet individual requirements, balancing personalisation with risk management.
“AI facilitates proactive customer outreach in embedded finance by utilising advanced data modelling to anticipate customer needs and offer pre-approvals for financial services, thereby simplifying access and enriching customer interactions.
“Furthermore, AI has automated and streamlined risk management and credit risk processes, areas traditionally reliant on labour-intensive, manual tasks susceptible to human error. Through predictive analytics and pattern recognition, AI systems proactively identify potential fraud and financial risks, enhancing the security and reliability of financial services. This automation signifies a shift towards more efficient, accurate, and secure financial ecosystems, highlighting AI’s pivotal role in advancing the embedded finance industry.”
Making payment and lending experiences frictionless
Matt Purnell, research analyst at Juniper Research, the analyst house specialising in fintech, explains: “Chiefly, AI has streamlined various processes of embedded financial services, creating more efficient experiences for many segments.
“For consumers, payment and lending experiences become frictionless due to AI, with the technology able to process loan applications and payments more efficiently. This, combined with customisation of aspects like loan offers reduces cart abandonment, increases consumer satisfaction, and therefore market growth as the conveniences offered by embedded financial services outweigh potential costs.
“Further applications for AI in embedded finance include improvements in data transparency and security. Irrelevant or low-quality data is a major hindrance for embedded financial services, and developments in explainable AI are being made to improve the reliability of data provided to companies, resulting in fewer errors and streamlining other processes like creditworthiness checks.
“As for security, generative AI is being used to simulate user activity patterns to create synthetic test data for training models and security evaluation systems. Whilst this is still relatively novel compared to other applications of AI in embedded finance, it is a segment that will become more refined to deliver models capable of creating highly efficient fraud detection capabilities.”
Enhancing customer experiences
Nelson Castellanos, chief partnerships officer at HDI Embedded, the embedded insurance company, discusses the benefits customers will receive thanks to AI: “AI has become a vital part of enhancing customer experience across the wider financial services space.
“For example, chatbots and virtual avatars that provide 24/7 support are now a common practice. As the technology improves, it’s likely we’ll see even more personalised guidance from these chatbots that can leverage data on customer preference and previous interactions to create a more seamless, customer-centric experience.
“Looking more specifically at AI for the embedded finance market, the trend of ‘hyper-personalisation’ continues. Take embedded insurance as a good example here. It already allows for dynamic pricing as insurance providers can tailor costs to the customer’s risk profile. However, AI’s ability to effortlessly handle and process vast datasets means embedded insurance providers can more accurately price policies, develop more precise risk models and streamline the assessment process.
“Finally, AI is reducing risk in the embedded insurance space. AI-driven fraud detection systems can identify and mitigate fraudulent activity and ensure greater market resilience. This means minimising false claims, identity theft and exaggerated/staged accidents.”
Targeting customer-centricity
Kurt Azzopardi, CTO of Andaria, an embedded finance provider, also appears to agree with the idea that AI is having a significant impact on the improvement of customer experiences: “In recent years, AI has significantly reshaped the landscape of embedded finance.
“Through the fusion of AI and embedded finance, there’s been a notable shift towards customer-centricity and seamless financial services. Financial institutions leveraging AI technologies are witnessing transformative changes within the embedded finance space.
“For instance, AI algorithms analyse user data to deliver personalised financial products and experiences within non-financial platforms. This integration not only boosts user engagement but also fosters greater financial inclusion by catering to diverse user needs. Additionally, AI streamlines decision-making processes, optimising operational efficiency and enhancing overall customer satisfaction and loyalty.
“As AI continues to evolve, its impact on embedded finance is poised to deepen further, with advancements expected to fuel innovation, improve risk management, and unlock new avenues for financial accessibility and empowerment within diverse digital ecosystems.”
Supporting personalisation
Finally, Yaacov Martin, co-founder and CEO of The Jifiti Group, breaks down how the embedded lending landscape is leveraging AI to good effect: “From an embedded lending point-of-view, one of the most significant benefits of AI is how it can help lenders and merchants personalise the customer loan journey, tailoring financing offers and recommendations based on a customer’s credit profile, purchase size and spending habits, in addition to assisting with real-time decisioning.
“AI can analyse a customer’s real-time financial data within the embedded platform through open banking and other third-party integrations—think bank account info or past purchases. This allows embedded lending platforms to present loan options that fit the customer’s current needs.
“For example, if a customer is about to make a large purchase, a personalised lending option—such as an affordable 36-month repayment plan—would be available at checkout.
“Predictive analytics is another game-changer for embedded lending, as it can help predict a customer’s future financial needs based on their spending patterns. This allows platforms to proactively suggest loan options to help customers with upcoming expenses, such as car repairs or home improvements.”
