Streamlining Credit Risk Assessment: The Advent of AI Credit Scoring

Improved Banking Access for Under-Banked Individuals

Traditional credit scoring methods often leave under-banked individuals, those with limited access to traditional banking services, ineligible for financial products due to their low or non-existent digital footprint when it comes to engaging with banking services.

AI credit scoring helps make banking services more accessible to under-banked individuals by using alternative data sources to evaluate their creditworthiness. Alternative data sources include but are not limited to rental payments, transactions on third-party FinTech apps, and utility bills.

This enables under-banked or non-banking individuals to gain access to essential banking services without having a prior or extensive credit history.

Improved Fraud Detection and Risk Mitigation

Traditional credit scoring methods often miss out on crucial data from unconventional sources. This increases the risk of threat actors successfully carrying out fraudulent and malicious activity, including stealing sensitive personal information and finances or assets.

By cross-referencing data from multiple data sources, including those outside of static, historical data sources, AI credit scoring systems can more easily identify suspicious financial activity. AI credit scoring can also continuously learn from new emerging threats, keeping its threat detection capabilities automatically ahead of the curve.

In partnership with chip manufacturer NVIDIA, American Express uses advanced, long short-term memory (LSTM) models to automatically detect anomalous activity, resulting in a 6% improvement in fraud detection capabilities.

Faster and More Efficient Approval Processes

Traditional credit scoring methods, especially when performed by a lone human assistant, can be slow, time-consuming, and cumbersome. This includes performing tasks like manually reviewing documentation, cross-referencing submission criteria and what has actually been submitted, and requesting modifications.

By utilizing AI-driven credit scoring systems, lenders can more quickly and easily make lending decisions based on real-time data. Instead of spending hours or possibly even days manually reviewing submissions, AI credit scoring systems can approve or deny requests in a matter of minutes or even seconds.

Greater Flexibility and Adaptability

AI credit scoring systems have the potential to continuously learn and improve over time. This helps make the systems faster and more efficient, as well as more adaptable at identifying emerging trends and patterns. This also ensures that credit scoring criteria remain consistently fair and compliant with regional and industry-specific regulations.

Many AI credit scoring systems are cloud-based as well, making them easy to integrate with legacy systems and scale as the financial institution’s needs grow. Technology partners like Orient Software can incorporate modular, cloud-ready AI credit scoring systems into financial institutions with minimal disruption, while retaining the essential capabilities of the existing legacy infrastructure.

Incorporation of Existing Biases and Unfairness

Machine learning algorithms are only as trustworthy and reliable as the data they’re trained on. Feeding the model insufficient, irrelevant, or outdated data may cause it to make biased or unfair decisions based on the prejudices present in the data.

By inadvertently exacerbating these biases, AI credit scoring systems risk being perceived as unreliable and non-compliant with regulatory standards, reducing their adoption throughout the financial sector.

At Orient Software, we develop, test, and deploy AI models that collect and analyze large, vast data sets from relevant unconventional data sources. The result is more fair and accurate predictions, significantly reducing the risk of potential bias and unfairness in decision-making processes.

Data Privacy Concerns

AI credit scoring systems raise a wide variety of data privacy concerns, including the collection and processing of personally identifiable information (PII).

To operate correctly, AI credit scoring requires a potential borrower’s historical data regarding their financial and borrowing activity. This includes data like bank statements, a history of past financial transactions, loan repayments, and even the frequency of text messages. As a result of having to collect non-regulated data, lenders and financial institutions that deploy AI credit scoring systems must abide by their relevant regional data privacy and security laws.

For places like the European Union, that means abiding by the GDPR (General Data Protection Regulation). Depending on which part of the United States you’re based in, you may be subject to state-specific data privacy laws. For example, California is bound by the CCPA (California Consumer Privacy Act).

At Orient Software, we develop robust, scalable, and secure AI credit scoring solutions that incorporate elements of fairness and transparency into their workflows. We make it easy for financial institutions to collect only relevant data with upfront user consent and compliance with relevant regional laws.

Poor Transparency

AI credit scoring systems are trained on complex machine learning algorithms, which collect and process huge amounts of data from unconventional sources. Due to the complexity of how these systems operate, it can be difficult for lenders and clients to grasp their potential advantages while easing their concerns regarding data privacy and biased decision-making.

For this reason, it’s important that technology partners educate financial partners on how these systems work, and that financial institutions pass on this information to clients in an easily understandable way. Clients have a right to know how such systems calculate their scores and the set of criteria they follow to reach certain conclusions.

One way to improve transparency is to incorporate transparent scorecards into the results. This involves describing what data sources were used to evaluate the data, along with how each data source is weighted against the others.

Calculating Loan Interest

By analyzing large amounts of data from unconventional sources, AI credit scoring systems can calculate the estimated interest of a loan for borrowers. They can factor in contributing details, including risk assessment, dynamic pricing, and personalization, tailoring the interest to the individual based on internal and external factors.

Approving Credit Cards and Determining Credit Limits

AI credit scoring systems can be used to determine whether clients have the ability to manage and repay a credit card. It can evaluate dynamic, unstructured data from multiple sources, so as to not just evaluate risk but also determine the optimal credit limit for the individual.

Approving Business Loans

AI has the potential to increase the speed, efficiency, and accuracy of approving complex, large-scale business loans that would otherwise take weeks through traditional means.

While some manual checks and balances are necessary, such as monitoring to rule out money laundering activity, AI credit scoring can help streamline the process of approving business loans.