They rely on member data from credit bureaus and financial models used by lenders to generate credit decisions that provide “high-certainty” financial products, whereby applicants are told in advance that there is a 90 percent or greater likelihood of their being approved.
As a result of this move to autonomous finance, consumers no longer have to apply for a loan or a credit card by sticking with their same bank or financial company and then enduring long wait times for possible approval. Now, customers can access one-stop shopping experiences via mobile and web applications from the comfort of their homes. Multiple autonomous online fintech platforms provide consumers the ability to peruse, for example, different credit card options and offer details, make an informed decision about a particular product, complete a pre-filled application, and receive a decision instantly, along with a temporary card or number that can be used immediately.
Tech-savvy banks are leveraging artificial intelligence (AI) and ML as part of their lending platforms for credit assessments and financial analytics. For example, banks can program ML models to predict bad loans, build credit risk models, and identify fraud risks. Additional applications include investment analysis and research. ML models can analyze vast data sets, including social media sentiment and news coverage to facilitate decision-making and enhance the effectiveness of some investment strategies.
Harnessing the technology
This ability to embrace the benefits of autonomous finance is due to myriad advanced technologies, including ML, AI, cloud technologies, distributed systems, recommendation engines, and extract transform and load (ETL) data pipelines. These technologies allow the creation of high-certainty offers to customers because before customers even apply for products, the established systems can pull their credit reports and run them against the ML models provided by the lenders.
These ML models are trained with vast amounts of data using thousands of data points, including an applicant’s income, employer information, and employment and housing status, to name a few. Based on this data, the models are trained to ensure even greater decision-making accuracy. Additionally, using this information, the models can analyze members’ financial situations, identify potentially troublesome patterns, and provide guidance on how members can improve their financial future.
Those decisions also extend beyond the consumer. The more autonomous finance is employed, the greater the capability the fintech industry has to use that information to help with economic disruptions. Because fintech platforms have millions of customers’ information (usually based on their credit reports, which are pulled every few days), the platforms can recognize trends and identify multiple issues, including:
- How many members apply for financial products
- How many members receive loans
- How many members default on their products or are behind in their payments
- Which members moved from the subprime to the prime market
- How many members can buy homes (based on how many home loan applications were received).
Addressing the challenges
Companies have several hurdles to overcome to reap the benefits of creating a fintech platform. Once the models are built, end-to-end encrypted data is required to ensure the safety and security of every individual’s data. The challenges first begin when initially building the required ML models. For them to work, these models need to mimic some of the credit decision engines used by the lenders. This requires a great deal of input from the lenders, which they are often (understandably) unwilling to share. Fintech platforms can provide model-building environments—systems created so lenders can build and deploy their models on the fintech platform. This workaround ensures that lenders don’t have to share their credit decision engines with fintech platforms.
Another major issue that companies should be aware of is the need for security, compliance, and transparency. When a lender builds a model, that model can’t simply be deployed to the platform and run against member data because this creates the potential for compliance issues. For example, if there is a bug in the model, members could see incorrect offers or offers with inaccurate information. Another issue arises when the fintech platform states that offers are high-certainty, and then applicants are informed they are ineligible for those programs. When platforms allow lending partners to build the models, the platforms can provide built-in guardrails to run the models against test data. Then, they can compare the results against existing models.
Embracing the future
While it is essential to establish the correct parameters in building these ML models, there’s no doubt that the future of the fintech industry is autonomous finance. According to recent research, the global autonomous finance industry generated $15.8 billion in 2022 and is expected to generate $82.6 billion by 2032.
With more people relying on their mobile phones and laptops for online shopping—from groceries to appliances—it’s only natural that there is a desire to have that same ease and speed when it comes to financial shopping via autonomous finance. Whether looking for personal loans, credit cards, or other financial products, fintech applications offer comparison shopping and a streamlined, simple application process. They can keep track of payments and manage users’ day-to-day finances. As people continue to shift online and away from traditional and in-person applications at their personal banks or finance centers, fintech applications are poised to become the financial assistants of the future.
About the Author:
Viswanadha Pratap Kondoju is an engineering manager with more than 14 years of experience in software engineering and seven years specializing in the fintech industry. He currently leads a team working on core platforms and services that enable millions of users to access personalized financial products and advice. He also has a background in full-stack development, machine learning, artificial intelligence, and cloud technologies. Viswanadha holds a Bachelor of Technology/Information Technology degree from the Indian Institute of Information Technology and a Master of Science in Computer Science and Data Science Track from the University of Texas, Dallas.
