In recent years, processes within the financial industry have become more automated. These include customer services, fraud detection and prevention, credit scoring, and credit risk analysis. There is also plenty of evidence that artificial intelligence is making its way into the bank accounts of customers. For instance, in the Philippines, two out of five companies are highly data-driven. Many companies are also starting to embed predictive analytics in finance and other daily operations.
As computers get smarter, banks and other financial institutions are using historical transactions and consumer databases to predict the future. Predictive analytics in finance also have the goal of minimizing costs and improving customer experience.
What is Predictive Analytics in Finance?
In a nutshell, predictive analytics is the use of data, machine learning techniques, and statistical algorithms to identify the possibility of future outcomes based on historical data. It goes beyond knowing what has happened to provide a comprehensive assessment and determine what will likely happen in the future, given current conditions. It’s keen to note that the term “predictive” doesn’t automatically mean the models always accurately predict the future. It means they deliver their best predictions based on the information available.
With financial predictive analytics, banks and other companies can find and exploit patterns contained within data to identify opportunities and detect risks. For instance, models can be designed to discover relationships between various behavioral factors. Such models can assess either the risk or promised presented by a specific set of conditions, guiding better-informed decision-making across various financial operations.
Benefits of Financial Predictive Analytics to Banks
Predictive analytics in finance can improve the daily operations of banks in multiple ways. In this section, we focus on how financial predictive analytics can help banks use existing data and identify trends to predict outcomes, expose hidden risks, identify untapped opportunities, and complete banking tasks quickly.
Predictive analytics in finance or credit scoring models use data to predict the creditworthiness of an individual. For instance, FinScore’s Telco Data Scoring Tool leverages telco data (call duration, call destinations, duration of SIM ownership, top-up amount, amount of load shared, etc.) to form predictive credit scoring.
Telco data is notably considered as the type of alternative data to have the most predictive power for measuring an unbanked individual’s creditworthiness. Telco usage is a great indicator of a user’s lifestyle and economic data. With over 76.5 million mobile phone users in the Philippines, banks and financial institutions rely on telecom data as sources for valuable information.
Sometimes, identity theft is entirely out of the control of banks and financial institutions. Even if you’re extremely careful, fraudsters can still easily commit synthetic fraud or identity theft when applying for loans, savings accounts, and other banking products. Straw borrowers or applicants with poor credit files can also choose not to disclose valuable information or intentionally misrepresent details in their application.
Banks with financial predictive analytics are better equipped to spot issues and mitigate them. Relying on traditional sources of customer data can lead to fraudulent practices getting overlooked. For instance, the Social Circle Fraud Tool, a fraud detection tool for finance from FinScore, compares an applicant’s recently contacted or most often contacted mobile numbers with a bank’s existing Fraudlist or Blacklist. It instantly identifies connections or links between applicants and previously identified fraudsters.
Banks and other lenders are becoming more sophisticated about how they evaluate applications for loans. They have begun to realize that not everyone has a high credit score – but that doesn’t mean they’re not qualified for loans. Some people don’t have a credit history, and others are still good borrowers even if they are unbanked. Predictive analytics in finance can help non-traditional borrowers get approved for loan products.
Credit Risk Analysis
In the banking sector, it’s crucial to have a strong understanding of your customers. Predictive analytics in finance helps banks collect up-to-date and accurate data to provide them with better insight in improving credit approval rates while reducing bad debt. Financial predictive analytics can help simplify the risk-profiling process, generating valuable information on credit-invisible customers in mere seconds. The insights gathered are often accurate in predicting the behavior of customers beyond transactions.
Unlock the Potential of Predictive Analytics in Finance with FinScore
Getting started in financial predictive analytics isn’t exactly quick and easy, which is why you need a committed partner who is willing to handle all the data analytics for you.
Whether you’re a commercial bank, a credit bureau, or a consumer loan company, FinScore’s products can help your company with predictive analytics in finance. With our cutting edge AI, advanced technology in machine learning, and committed team of experts, FinScore has been at the forefront of delivering alternative data and predictive analytics as effective credit risk, fraud prevention, and credit scoring solutions. We’ve onboarded one of the country’s biggest brands in the financial industry and have a long-standing partnership with SMART.
For more information and inquiries on how we can help you get started with financial predictive analytics, don’t hesitate to contact us today.