From facial recognition and digital voice assistants to social media algorithms and chatbots, artificial intelligence (AI) is embedded in our daily lives. This ability to intersect with and enhance everyday tasks has accelerated its adoption: 35% of companies are using AI in their business, with a further 42% exploring its use – and the financial services sector is leading the charge. 

Nowhere is this more evident than in a disruptive sphere of this traditionally clunky sector: embedded finance. This insertion of financial services into nonfinancial platforms, to streamline processes and enhance user experiences, is being elevated by AI throughout every stage of the process – with compelling benefits.  

Enhanced customer experiences 

By wedding embedded finance with AI, brands can give their customers exactly what they want: interactions with loans, accounts, and payments that mirror the highly personal and frictionless experiences they enjoy elsewhere online. 

To achieve this, AI replaces narrow data sources based on demographics and age with real-time analysis of vast datasets – such as browser history, social interactions, and past purchases – empowering brands to recommend targeted products and services based on customers browsing and buying preferences.  

This insight allows them to anticipate their expectations and offer them what they want at the right time as part of an integrated, personalised omnichannel experience – enhancing customer engagement, satisfaction, and loyalty. 

Cost reduction 

The cost-saving potential of integrating AI-powered solutions into embedded finance is multifaceted: 

• Automation of repetitive tasks: AI automates routine and repetitive tasks such as data entry, transaction processing, and document verification. This eliminates manual intervention, saving time and labour costs. 
• Enhanced operational efficiency: AI algorithms optimise operations by streamlining processes and identifying bottlenecks. This increased efficiency leads to lower operational costs. 
• Credit risk assessment: AI-driven decisioning assesses credit risk more accurately by conducting real-time analysis of broad and diverse data sets. This enables businesses to make better lending decisions, reducing the risk of defaults and associated costs. 
• Fraud detection and prevention: AI-powered fraud detection systems can analyse vast amounts of transactional data in real time and identify suspicious patterns and trends expeditiously. This helps to mitigate fraudulent activities and minimises the associated financial losses. 

Operational efficiency 

The core aim of embedding financial services directly into non-financial platforms is to make transactions seamless by circumventing fragmented processes that rely on third parties. This innate ability to improve operational efficiency from the moment a customer engages with a brand through to the end of their journey is supercharged by AI. 

This transformative technology leverages vast historical and behavioural datasets in real-time to streamline workflows, identify inefficiencies, automate processes, better understand customers, and accelerate and improve decision-making – liberating businesses from siloed data, manual processes, and short-sighted choices.   

For example, AI can be used to automatically horizon scan the regulatory landscape for new rules and regulations, or amendments to existing ones. Having replaced potentially harmful manual processes that are prone to human error with automation, it notifies stakeholders and produces performance data that enables preventive action if workflows become, or are at risk of becoming, non-compliant.  

Market competitiveness 

Embedded finance, augmented by AI, creates a symbiotic relationship between the innovative platforms that power it and the brands that embrace it, who benefit from a competitive advantage by adding extra layers to their offering – such as personalisation, enhanced credit decisioning, improved security, and robust regulatory compliance.  

Take embedded lending, for example. Credit decisioning is a fulcrum of this subset of embedded finance. AI vastly improves credit-approval times and percentages by providing forward-looking insights into an applicant’s suitability to repay a debt obligation in the future and presenting its findings in real-time, rather than depending on historic credit reports. 

This comprehensive and expedited approach allows nonfinancial businesses that embed lending into their customer journey to make informed decisions that break down barriers to financial inclusion. For SMEs, this AI-driven decisioning unlocks access to time-critical funds that can mean the difference between survival and ceasing to exist – and can’t be obtained as seamlessly elsewhere. 

For example, Liberis offers a revenue-based lending model driven by an intelligent data engine that automatically forecasts business transaction revenues and makes a personalised and preapproved offer instantaneously – with 70% of businesses receiving their funding in less than 48 hours. 

Increased revenue 

These multifaceted advantages of integrating AI into embedded financial services combine to drive increased revenue growth – from personalisation that stimulates repeat business and cross-selling opportunities to accurate credit decisioning that increases the spending power of customers.  

Amid the accelerated evolution of this transformative space, it’s estimated that embedded financial services will produce $384.8 billion in revenue by 2029 – almost a 17x increase over the $22.5 billion in revenue generated in 2020. Fuelling this exponential growth is AI and the compelling advantages of its intersection with this new distributed approach to providing financial services. 

Unlock the power of embedded finance and AI 

Embrace the transformative capabilities of embedded finance and AI to enhance customer experiences, reduce costs, boost operational efficiency, and drive market competitiveness. 

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