The rise of fintech companies and solutions has drastically changed how lending works for both consumers and entities. Initially, transformation in the lending industry began with peer-to-peer loans in conjunction with the 2008 financial crisis. Companies first began to use alternative data for their lending decisions. Alternative data refers to any information about a company or a consumer that is not captured by traditional data sources or credit scoring methodologies. Companies were merging traditional and alternative data sets to get more holistic views of potential customers, which allowed many consumers to qualify for loans that they otherwise would not be able to get with a traditional credit scoring method.  With 1.7 billion people underbanked worldwide, alternative data could make loans more accessible and create financial inclusion for a broader community.

Financial institutions have gone through a transformation over the past few years driven by technology innovation. This transformation has been driven by new entrant fintech players that have challenged large established institutions by changing the way consumers and entities interact with financial services such as payments, lending, insurance, personal finance, money transfers, and consumer banking. Much of the improvement to financial infrastructure has been driven by improved communications between systems and databases. Companies like Plaid and MX have changed back-end infrastructure by improving the interactions among the network through application programming interfaces (API). Tune into our latest State of Identity Podcast with MX on Modern Connectivity and Open Finance. 

APIs can help banks significantly improve inter-system connectivity by linking disparate systems within a bank’s network, easing processes such as applications for mortgages, consumer loans, and deposit accounts. By utilizing APIs, lending businesses could improve their product offering instead of spending significant time and resources on rebuilding whole systems. In the investments space, lending tech companies have garnered mainstream attention and investment – Blend is a cloud-based platform that supports and simplifies applications for mortgages, consumer loans, and deposit accounts, which recently went public with a $4 billion valuation.

Other improvements to financial institutions were supported by data aggregation providers like Yodlee and Plaid. Data aggregators have become ubiquitous for banks to provide customers with capabilities like a holistic view of their finances or as a way to track spending. By doing so, financial institutions gain access to previously disparate data while enabling greater functionality for customers.

Ability to capture the correct data 

With the growth of data collection (it’s expected to reach 175 zettabytes by 2025, up from 59 zettabytes in 2020), financial institutions face the challenge of determining what types of data actually matter. As more people use digital services, institutions are able to collect increasing amounts of data. Subsequently, lenders need to sift through burdensome volumes of data signals to understand an applicant’s creditworthiness to ensure they can genuinely pay off a mortgage or other loan. 

This requires understanding what data matters to the organization and for what specific needs. Checking account opening requires differing data points than loan applications; neither process requires collecting every piece of (potentially immaterial) data to make an informed decision. Furthermore, the storage of unnecessary sensitive personal data should be weighed as privacy concerns and breaches grow worldwide. By reducing the data pull to what matters, organizations protect themselves from privacy concerns and breaches while developing more substantial results. 

Data analytics show the way

Financial institutions turn to these APIs because they don’t want to invest in building the application themselves but don’t have the capability to aggregate financial data across the organization and use it effectively. Lending tech companies have switched from gathering data themselves, as they can partner or access data from multiple aggregators, to focus on analyzing and delivering insights from the data. 

Lending tech and fintech have incorporated data analytics, artificial intelligence (AI), and machine learning (ML) into their products to analyze customer financial data. These technologies deliver actionable insights, enable predictive analytics, and offer organizations new ways to reach out, provide guidance, and build new revenue streams. Companies hold a trove of data that would enable them to incorporate their findings — along with the data aggregators and APIs — to build accurate results from these analytics.

Financial Identity 

Through data aggregation and data analytics, lending tech companies are creating new financial identities for each citizen and entity, which is integral to equality of opportunity, quality of life, and overall well-being of both individuals and businesses. Financial inclusion is a critical developmental challenge and is currently woven into seven of the UN’s 17 Sustainable Development Goals. By expanding financial identities to marginalized individuals and extending increased access to mobile money, financial service providers have an opportunity to reach 1.7 billion new retail customers and increase loan volumes by $2.1 trillion. Public and private partnerships can expand their footprint and influence and improve national, regional, and global identity infrastructures. 

This trend in lending technology and fintech services highlights data's importance, providing valuable insight for customers. The ultimate communication between data providers and entities will come with the evolution of open banking and the rise of regulation for information communication and standardization like PSD2. A new era of open banking will enable systems to integrate quickly and seamlessly with emerging platforms and applications, and robust networked digital ecosystems will quickly replace physical banks and paper systems.