Fintech has gained popularity due to advancements in technology, making financial services efficient, accessible, and user-friendly.
It is a constantly evolving industry driven by new technological developments and trends.
Here are the four most important fintech trends in the age of artificial intelligence (AI) to watch out for!
Embedded finance
Embedded finance is a relatively new concept that has been gaining traction in recent years.
Essentially, it refers to the integration of financial services into non-financial products or services. This can include everything from banking services integrated into e-commerce platforms to insurance offerings built into ride-sharing apps.
One of the main benefits of embedded finance is that it allows companies to offer a more seamless and convenient user experience for their customers.
By integrating financial services directly into their existing products or services, companies can reduce friction and make it easier for customers to access the financial tools they need.
It can also help drive growth for both the companies offering these services and the financial institutions providing them.
For example, by partnering with fintech start-ups or other non-financial companies, traditional banks can expand their reach and tap into new customer segments.
AI-driven experiences
Revolutionising the banking industry, AI technology is providing hyper-personalized customer experiences.
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Traditional banks are struggling to keep up with fintech companies that use AI to offer better customer experiences. Open banking APIs allow fintech companies to access bank accounts and debit cards, giving them an advantage over traditional banks.
Adopting AI-driven customer experiences is becoming a norm in the financial services industry. By using AI to analyse customer data, banks can offer tailored recommendations and solutions to their customers.
Personalised lending models
Enabling financial access for underserved populations is vital for achieving financial inclusion. Traditional financial institutions have often fallen short in this regard, leaving many individuals without access to loans and credit.
Enter alternative lending start-ups, revolutionising the industry with personalised loan and credit options.
Through the power of technology and data analytics, these start-ups tailor loans to meet individual needs and circumstances.
For example, some start-ups consider unconventional criteria like rental history or utility payments when providing mortgage loans, expanding opportunities for home ownership beyond traditional lenders.
In addition, alternative lending models leverage machine learning (ML) to effectively detect and prevent financial fraud.
Such fraudulent activities can have severe consequences for individuals’ financial information and savings. By embracing ML models, these start-ups protect consumer interests while ensuring the availability of reliable financial services.
