1. Home
  2. credit scoring

credit scoring

How can AI be used to improve credit scoring?

AI can analyze data for accurate risk assessment, reduce bias, automate tasks and personalize the lending experience for improved credit scoring.

Artificial Intelligence (AI) can be used to improve credit scoring in a number of ways. Credit scoring is the process of assessing a borrower’s creditworthiness based on their credit history, financial data and other relevant factors. Here are some ways AI can improve credit scoring:

Better data analysis

AI can analyze large volumes of data from a variety of sources to identify patterns and trends that might not be apparent to human analysts. This can help lenders make more accurate predictions about a borrower’s creditworthiness. The below steps illustrate how AI can be used for data analysis:

  • Collect data from various sources, such as social media, credit bureaus and financial statements.
  • Pre-process and clean the data to ensure it’s ready for analysis.
  • Apply machine learning algorithms to the data to identify patterns and trends.
  • Use the insights gained from the analysis to inform lending decisions.

Improved risk assessment

AI can be used to build predictive models that assess the likelihood of a borrower defaulting on a loan. These models can take into account a wide range of factors, such as income, debt-to-income ratio and payment history, to better predict the risk associated with lending to a particular borrower.

The general steps followed by lenders to assess borrower’s suitability for credit are listed below:

  • Collect data about the borrower, such as credit history, income, employment status and other relevant factors.
  • Pre-process and clean the data to ensure it’s ready for analysis.
  • Train machine learning models on the data to predict the likelihood of a borrower defaulting on a loan.
  • Use the models to assess the risk associated with lending to a particular borrower.

Reduced bias

AI can help reduce bias in credit scoring by using objective criteria to assess creditworthiness. This can help reduce the impact of factors such as race, gender and ethnicity on lending decisions.

One of the challenges in credit scoring is ensuring that the process is fair and free from bias. Historically, lending decisions have been influenced by factors such as race, gender and ethnicity, which can result in discriminatory practices. However, with the use of AI, it’s possible to reduce the impact of these factors on lending decisions.

To achieve this, lenders need to identify potential sources of bias in the credit scoring process, such as race, gender and ethnicity. They can then train machine learning models to exclude or de-emphasize these factors in the lending decision process. By doing so, lenders can make more objective and fair lending decisions that are based on the borrower’s creditworthiness rather than personal characteristics.

However, it is critical to note that AI is not immune to bias, and it’s crucial to monitor the models for any signs of bias and adjust them as needed to ensure fairness and transparency. This necessitates constant model monitoring and assessment, as well as routine evaluations of the training data. By doing so, lenders are able to guarantee that their credit score system is impartial and equal for all borrowers, regardless of their racial, gender or cultural background.

Related: Ethical considerations in AI development and deployment

Faster processing

AI can significantly improve the speed and efficiency of the credit scoring process. Traditionally, credit scoring has been a manual and time-consuming process, involving a lot of paperwork and human intervention. However, with the use of AI, lenders can automate many of the tasks involved in credit scoring, reducing processing times and increasing efficiency.

One of the ways AI can speed up the credit scoring process is by automating data entry and analysis. By using machine learning algorithms to process and analyze large volumes of data, lenders can quickly assess a borrower’s creditworthiness and make lending decisions in real-time. This can be especially useful for online lending platforms that require fast and accurate credit assessments.

Another way AI can improve the speed of credit scoring is by automating the loan application process. By using chatbots and other AI-powered tools, lenders can provide borrowers with instant feedback on their loan applications, reducing the time and effort required to apply for a loan.

Improved customer experience

 AI-powered credit scoring can provide borrowers with a more personalized lending experience. For example, lenders can use AI to offer borrowers loan products that are tailored to their specific needs and financial situations. The lenders typically follow the steps below to enhance the borrower’s experience:

  • Collect data about the borrower, such as their financial goals and risk tolerance.
  • Use machine learning algorithms to identify loan products that match the borrower’s needs and preferences.
  • Offer personalized loan products to the borrower based on the analysis.

Related: 7 potential use cases of chatbots in banking

Ex-Investment Banker and Registered Broker Busted in $1,500,000 Crypto Fraud Scheme: DOJ

9 examples of artificial intelligence in finance

Discover how artificial intelligence is transforming the financial sector with nine examples of AI in finance.

Artificial Intelligence (AI) is transforming the financial sector, revolutionizing how banks, financial institutions and investors operate. Here are nine examples of AI in finance, and how they are changing the industry:

Fraud detection

AI algorithms can analyze transactions in real time, detect anomalies and patterns that may indicate fraudulent activities, and alert banks to take appropriate actions. An example of fraud detection using AI is PayPal’s fraud detection system. PayPal uses machine learning algorithms and rule-based systems to monitor real-time transactions, and identify potentially fraudulent activities.

The system examines data points like the user’s location, transaction history, and device information to identify abnormalities and patterns that can hint at fraudulent behavior. The technology can notify PayPal’s fraud investigation team about a possibly fraudulent transaction so that they can look into it further or block the transaction. The amount of fraudulent transactions on the network has dramatically decreased thanks to this AI-powered solution, making using PayPal safer and more secure.

Customer service

AI-powered chatbots can provide personalized financial advice, answer customer queries, and automate routine tasks like opening new accounts or updating customer information.

The chatbot “KAI” from Mastercard, which helps clients with account queries, transaction histories and expenditure tracking, is an example of how AI is being used in customer support. KAI uses machine learning algorithms and natural language processing to offer consumers tailored help and financial insights across a variety of channels, including SMS, WhatsApp, and Messenger.

Algorithmic trading

AI can accurately assess past and present market trends, spot patterns, and predict future prices. AI algorithms can also perform transactions in real time, using pre-programmed rules and conditions, optimizing investing strategies and maximizing returns.

Financial institutions and investors benefit significantly from this technology, which enables them to make data-driven decisions and maintain an advantage in the fiercely competitive world of trading.

Related: What are artificial intelligence (AI) crypto coins, and how do they work?

Risk management

By analyzing complex financial data, artificial intelligence can identify potential risks and forecast future scenarios, providing valuable insights that enable banks and other financial institutions to make well-informed decisions. 

An example of risk management using AI is BlackRock’s Aladdin platform. To analyze enormous volumes of financial data, spot risks and opportunities, and give investment managers real-time insights, the Aladdin platform combines AI and machine learning algorithms.

By examining elements like market volatility, credit risk, and liquidity risk, the platform assists investment managers in monitoring and managing risks. Investment managers may enhance their investment strategies and make data-driven decisions thanks to Aladdin’s risk management capabilities, which lower the risk of losses and boost returns.

Portfolio management

AI can analyze vast amounts of financial data and provide insights into investment trends, risks and opportunities, helping investors make informed decisions. An example of portfolio management using AI is Wealthfront, a robo-advisor that uses AI algorithms to manage investment portfolios for clients. 

To create customized investment portfolios for clients based on their goals, risk tolerance, and financial position, Wealthfront combines classic portfolio theory and AI. As market conditions and the client’s goals change, the platform automatically rebalances the portfolio while continuously monitoring its performance. Many investors find Wealthfront an appealing alternative because of its AI-powered portfolio management, which enables customized and optimal investing plans.

Credit scoring

AI algorithms can analyze credit histories, financial statements, and other data to provide accurate credit scores, enabling lenders to make better lending decisions. For instance, ZestFinance’s Zest Automated Machine Learning (ZAML) platform uses AI to analyze credit risk factors and provide more accurate credit scores, improving lending decisions and reducing the risk of default.

Personalized financial advice

AI-powered robo-advisors can provide personalized financial advice and investment strategies based on a client’s financial situation, goals and risk tolerance. For instance, Bank of America’s AI chatbot, Erica, can provide personalized financial advice, answer customer queries and automate routine tasks.

Insurance underwriting 

AI can analyze a range of data points, including demographic information, health records and driving history, to provide accurate insurance underwriting. For instance, to improve accuracy and lower fraud in the insurance market, Lemonade, an AI-powered insurtech company, employs AI algorithms to evaluate claims and underwrite insurance policies.

Related: A brief history of artificial intelligence

Regulatory compliance

AI can help financial institutions comply with complex regulations by analyzing transactions, detecting fraud, and ensuring compliance with Know Your Customer and Anti-Money Laundering regulations. 

For instance, ComplyAdvantage helps businesses comply with legal obligations and avoid fines by using AI and machine learning algorithms to monitor financial transactions and identify potential money laundering activities.

Ex-Investment Banker and Registered Broker Busted in $1,500,000 Crypto Fraud Scheme: DOJ