Lending Tech: Better Data for Better Decision-Making

By: Liminal Team

Alternative data, financial identity, and technology innovations around financial services infrastructure has led the change in lending. 


Traditionally, consumers had a 1-1 relationship with financial institutions (i.e., banks and credit unions) to underwrite lending products such credit and mortgages.


Traditional Relationship

Trusted sources of financial account and transactions data

Trusted sources of credit and appended identity data

Financial institutions are a ubiquitous source of consumer financial account and transactions data and are also responsible for performing enhanced due diligence on customer usage.

Financial institutions relied on credit bureaus and traditional data sources for identity and credit information that would be used to determine creditworthiness.

The introduction of consumer lending apps drastically changed how lending works for both consumers and entities


Consumer Lending Apps

A UX-first approach digitalized the customer journey and attracted new hard-to-reach audiences that were otherwised unbanked, thin-file, or credit-invisible.

This transformation has been driven by data aggregators that have challenged the way established institutions gather, analyze and share identity data. Data aggregators have merged traditional and alternative data sets to get a more holistic view of potential costumers and to verify assets/employment/income (VOA/VOE/VOI).


Data Aggregators

Data Aggregators have emerged as trusted intermediaries between fintech platforms and financial institutions to compile financial account and transactional data through authorized API partnerships and/or screen-scraping of consumer-provided credentials.

This new data flywheel has also birthed additional entrant fintech players that provide financial institutions with integrated, value-add services to automate the loan origination process. 


Fintech Players

They too, rely on data integrations across the digital identity landscape to assists with credit decisioning identity verification, authentication, fraud detection, and identity theft protection. 

Each discrete layer in this “lending tech” stack – financial institution, data aggregator, credit bureau, fintechs, and consumer – are part of a larger data-sharing ecosystem underpinned by identity data. 



Data triangulation helps to meet regulatory compliance requirements, to verify identity, and to prevent fraudulent lending. More importantly, the new evolving fintech and lending ecosystem has evolved the way consumers engage, qualify, and access lines of credit they may otherwise be excluded from. 

Trusted sources of financial account and transactions data

Trusted sources of credit and appended identity data





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.

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