Robots are coming to take over!!
But we don’t have to worry about it. Finance industry is adopting the tech transformation rapidly. Like several other industries, banks have started using machine learning and artificial intelligence too. In fact, machine learning in banking is one of the hottest trends currently. But, the question here is, how do banks use machine learning in financial services, and what exactly does machine learning help them with? Well, well, we are coming to that.
Previously there have been many cases of practicing biases against people of different genders, races, and sexual orientations. Banking institutions were prejudiced in terms of who was eligible for the credit and who was not. With machine learning and AI in the finance industry and banking sector, the whole process of loan disbursement relies on algorithms rather than any human judgments and errors. The prime reason behind this shift was to make loan disbursement simpler and unbiased for everyone.
Machine learning has brought in a new era of digitization of financial transactions, including the whole process of lending, right from filling the loan application to loan disbursement. In this insightful blog, we are going to uncover the various roles of AI and machine learning in finance and loan disbursement in detail. So, stay with us while we take this discussion ahead.
Before we begin with the core idea of the blog, let us get our basics clear. Let’s understand what machine learning is to begin with.
What is machine learning?
Machine learning falls under the umbrella term artificial intelligence, which allows the software application to become more accurate at predicting outcomes based on past history and data. It uses historical data to predict new output values. As some of the biggest companies like Facebook, Google, Netflix, and Uber are making machine learning a central part of their business operations, its popularity is increasing like wildfire.
Taking inspiration from these big companies, more and more industries, both big and small, are using machine learning algorithms to understand their customer behavior and business operation patterns better than ever.
How does machine learning transform credit profiling for banks?
Machine learning algorithms gather knowledge at an oversize scale. It then consolidates this knowledge to establish patterns from its own depository history. Further, based on this knowledge, the banks decide whether a borrower is creditworthy or not. Thus, machine learning helps the banks in improving their algorithms by ensuring that they understand the credit and market patterns better.
Find below a few companies that are leading in credit analysis.
CoinTribe is one of the well-known online loan disbursement platforms that provides quick and easy loans to small businesses and other individuals. This is the only lending platform that has back-tested its credit model with large banks. The marketplace model enables loan origination and credit evaluation through CoinTribe before sharing it with the banks.
Aye Finance is a commercial institution built with a mission to solve the challenge of funding MSMEs and include them in the mainstream economy. It functions a little differently by creating a technically enabled process that builds credit insights based on a variety of available business and behavioral data.
Satya microCapital is the NBFC-MFI serving low-income entrepreneurs in rural and urban areas of India. This firm is known for providing convenient and affordable collateral-free credit to unbanked and underserved people. This firm follows a strong credit assessment and centralized approval system to provide credit efficiently.
MoneyTap is an app-based consumer credit line based out of India. The credit line here means that the bank will issue a limit up to 5 lakh without charging any interest. Using the MoneyTap app, the consumer can borrow any amount between Rs. 3000 to 5 lakh and then pay it as EMIs in two to three years. This app securely connects with the banking system to provide the consumer with a quick approval and credit limit depending on their credit history.
Happy evaluates more than 1000 factors regarding the merchant to support their micro business. Its credit model is based on the degree of commercial interaction a merchant has with a partner, his borrowing habits, APIs, business market trends, and demographics. Further, happy loans provide customized loan offerings to microenterprise owners so that they can meet their specific demands.
How does machine learning aid in the transformation of Credit Underwriting Software?
As we said above, machine learning is helping banks with loan disbursement in more than one way. The whole tedious and time-consuming process of credit underwriting has also been transformed by machine learning.
Let’s first see what credit underwriting software is.
Credit underwriting used to be a long and tedious process for borrowers with traditional banking and other offline sources. With credit underwriting software, credit lending is possible via digital platforms. This has led to major changes in credit underwriting to keep up with the diverse needs of the public. Credit underwriting software reduces all the operational bottlenecks and streamlines the lending process.
Here are the ways machine learning and other related technologies impact credit underwriting software. Have a look ahead.
With machine learning, lending financial institutions can find complex and relevant variables in the data that were not visible earlier. Easy access to this information allows lenders to choose borrowers for loans. Moreover, machine learning models can track and monitor all the incoming payments to track the defaulter loan as well as calculate the loan defaulter score.
Mortgage lending requires a lot of paper-intensive documentation for each application but well, not anymore. Machine learning automates routine tasks and sets criteria for whitelisting potential customers. Machine learning tools approve or reject borrowers by scanning documents and auto-categorizing them without any human intervention. Thus, in a nutshell, all the tedious manual documentation is taken care of by machine learning digitally.
Machine learning-based automation allows lenders to assess creditworthiness and risk parameters in an automated lending solution. This enables replacing the conventional credit assessment which is often time-consuming. The automated mechanism with machine learning allows inexperienced personnel to screen loans quickly by providing a faster turnaround time for customers.
Generally, the traditional lending process used to be quite lengthy as banking and non-banking companies take a few weeks to approve a loan. However, the introduction of the digital lending process with the power of RPA and ML allows the users to receive credit approval quickly. Machine learning and other AI technologies use factors to approve the loan for customers who meet the criteria. Few of these factors include online purchases, utility bill payments history, and social media profiles.
Machine learning in construction loan management- Here are the insights.
Today, Fintech, AI, and machine learning in the banking sectors have become quite common. The unbelievable impact of these advanced technologies on construction loan management and process automation has made loan disbursement simpler for banks as well as customers.
In addition, technologies like Fintech and machine learning are highly preferred for loan management and disbursement as they save up to 90% of the cycle team and eliminate human errors.
Moving ahead, we will focus on the ways machine learning is impacting construction loan management today. So, here we go!
- Software and process automation using machine learning reduces the cost of spending time as well as a massive amount of repetitive tasks in loan management.
- Advanced machine learning identifies compliance-based issues in loan disbursement and eliminates any risk of human errors.
- Together, AI and ML can transform financial services by speeding up the process and tedious tasks involved in construction loan management.
And now, we will see how advances in artificial intelligence and machine learning help commercial lenders. Read on.
- Identifying bottlenecks in the operation workflows and improving the process efficiencies and efficiency ratios.
- Using repetitive process automation to free employees and focus on skill-based work for a more engaging experience for the customers.
- Reducing errors and risks that banking businesses and industries often encounter with the traditional credit lending process.
Want to integrate machine learning into the loan disbursement process?
As we walked through the many ways machine learning is helping in revolutionizing loan disbursement today, we hope you understood how much it could benefit in turning your business upside down.
If you work in the financial sector and want to stay ahead of the competition, you need to get away from the traditional banking and credit disbursement systems. Not only are they time-consuming, but they are also prone to risks and errors. On top of that, the employees often discriminate while lending credit.
These problems and many more could be solved by implementing AI and machine learning in finance sectors and loan disbursement. It’s an exciting time for the banking industry as technology just keeps getting advanced with time. Furthermore, machine learning for financial services is benefitting some of the top banks in the world, and it is only going to grow in the coming future.
If you can’t wait to give your banking business a new life with AI and machine learning, seek expert advice now. Tech upgrades, improved sales, data security, reduced paperwork, and staying ahead of the competition, all of these are the top benefits of machine learning in Fintech. If you want your banking business to achieve all this, why delay using machine learning for financial services? For the best assistance and guidance throughout, reach out to the industry experts today.
Our expert developers at Kody Technolab have worked on many Fintech solutions and can help you navigate your journey efficiently. Drop us a message or talk with us now; we are always happy to assist you.