The future of customer-centric lending with machine learning

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In today’s era of data-driven decision-making, the marriage of machine learning and open banking data is transforming financial services. In recent years, we’ve seen its successful applications across various domains, including enhancing fraud protection through the analysis of extensive datasets with sophisticated algorithms that identify patterns indicative of fraudulent activity. 

The technologies have also played pivotal roles in algorithmic trading with real-time analysis of market trends, and in supporting regulatory compliance, where it has helped financial institutions in meeting and navigating complex regulatory requirements. The financial services industry has shown others that it is dynamic, and adopting evolving technologies has certainly played a core part of this evolution.

Now more than ever, machine learning and open banking data are also poised to shake up the lending landscape. The convergence of these technologies presents a real opportunity for lenders to better understand their customers, personalize their products as a result, and foster a more transparent and responsive lending ecosystem.

In this article, I delve into my thoughts on the three key ways machine learning technology is redefining the game for the lending industry, and where the opportunities are to offer mutually beneficial outcomes for both lenders and customers alike.

Nick Allen

Chief Technology Officer at Aro.

Marrying Open Banking data with machine learning

One prevailing trend that the lending industry can take advantage of is the increasing demand for personalized products from customers. The fusion of machine learning and open banking data is becoming a linchpin for how lenders engage with their customers, increase their satisfaction and build brand loyalty. Moreover, the pairing of the open banking data and machine learning algorithms enables lenders to gain unparalleled and deeper insights into customer profiles. With access to rich insight of approximately a hundred individual attributes (including data from utility payments, rental history, public records, spending habits etc.), lenders can assess their customers’ creditworthiness more accurately to customize financial products, ensuring that they respond to customers' specific needs and financial capabilities. For example, this could lead to the introduction of credit options that are presently unavailable or even result in lower interest rates for customers who connect their data and demonstrate their sustainable affordability.

What’s more, the incorporation of open banking data introduces a layer of transparency and accuracy to the credit matching experience. Not only do borrowers benefit from a more holistic evaluation that goes beyond the archaic credit scoring approach, but they also get a fairer representation of their financial standing with real time and accurate data. This not only instils more confidence in the lending process, it also boosts financial inclusivity by offering opportunities for individuals who may have limited credit history, despite exhibiting responsible financial behaviors.

Improved personalized credit matching

The adoption of machine learning and its advantages should also extend beyond lenders' internal operations. While borrowers now anticipate tailored offerings from lenders that align precisely with their unique financial requirements and capabilities, achieving this high degree of customization demands more than just implementing the latest cutting-edge technology. In fact, it requires a nuanced understanding of borrowers' behaviors and preferences, emphasizing the importance of a customer-centric approach that goes beyond assessing surface-level data. Advanced machine learning algorithms are now capable of evaluating customer profiles against available financial offers, boosting offer acceptance and completion rates. This approach levels the playing field for lenders, keeping the best interests of customers at the forefront.

Before now, many customers were excluded from accessing credit services through no fault of their own. Thin credit files, bias affordability calculations and one-size-fits-all credit decisioning has left many unable to access credit they can afford, or matched with unsuitable products. Machine learning algorithms, however, bring objectivity and speed to this process. Notably, machine learning algorithms can streamline the loan application processes by rapidly analyzing open banking data to improve the overall customer experience and efficiency of lenders. For instance, with machine learning, credit decisions have gone from taking days, to a matter of hours.

In addition to individual credit assessments, machine learning algorithms empower lenders to stay ahead of dynamic market conditions. In particular, lenders with this technology can continually analyze market trends, customer preferences and other economic indicators in real time. These algorithms can be crucial to provide lenders with valuable insights for strategic decision making when it comes to developing products and risk management in times of economic downturn.

Empowering consumers to navigate the complexities of personal finance

The advantages of machine learning are not exclusive to lenders. It is also becoming a powerful tool to enhance financial literacy among customers. By analyzing their income and expenditure data, machine learning can provide customers with personalized insights into their financial health to highlight what they can afford, and ultimately enable them to make more informed borrowing decisions.

Financial literacy is the cornerstone of a responsible lending environment. As customers gain insights into what they can afford, they become more aware of their financial capabilities and potential risks. Machine learning, in this context, acts as an educational guide, promoting transparency and responsible borrowing practices. The result is a customer base that is more financially savvy and less susceptible to pitfalls associated with uninformed financial decisions.

Entering a new frontier of optimized credit matching

As these innovative approaches continue to gain traction in financial services, the integration of machine learning and open banking data is expected to bring about a more efficient and customer-centric lending ecosystem. Lenders equipped with a robust machine learning approach are those who will better serve their clients, offering tailored solutions, while customers gain the ability to make more informed financial choices, fostering a responsible and transparent lending ecosystem.

In the coming years, the marriage between machine learning and open banking data will continue to evolve further to unlock new possibilities for the lending sector and broader financial services industry. It’s an exciting time for the lending industry and with a focus on customer-centricity and the responsible use of data, we’ll see the lending landscape undergo welcomed change from lenders and consumers alike.

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Nick Allen is Chief Technology Officer at Aro.