Machine learning is enthusiastically integrating into our lives and changing how we make decisions every day. Machine learning technologies leverage historical data and user behaviours to predict patterns and make automated decisions. The rapid pace at which the machine learning technology is accelerating is leading businesses to change the way they operate.
Applications of Machine Learning in Financial Services
Machine learning in finance has been making the rounds successfully due to its ability to tailor the user experience to suit the unique needs of the customers. The retail banking sector can thrive with machine learning capabilities like automated fraud detection and creditworthiness assessment, etc.
Machine learning in finance when paired with technologies like predictive analysis, escalate the scope of personalisation, product recommendations based on behaviour patterns as well as facilitate routine transactions like paying bills and account checking.
Although the traditional financial industry was not the front runner when it comes to ML adoption, machine learning in the fintech sector is common.
Here are some of its most common applications:
Robotic Process Automation
The transition of lending companies from traditional approaches to cloud-based systems brings forth a tremendous opportunity for further growth in the industry. Moreover, the advent of machine learning technologies can further automate the client-facing process at a lending firm. Fintechs can attain optimal operational efficiency by integrating machine learning platforms or aggressively data-centric processes and save time as well as resources.
Besides automating client-facing operations, machine learning can interpret documents, analyse complex data and come up with intelligent solutions for complicated problems. Machine learning technology’s predictive powers can identify issues without any human intervention even before they occur. A lot of data searching and sorting, which took hours to perform manually, can now be done in minutes and even in seconds. Through real-time edit for error resolution and regulatory compliance systems backed by machine learning, the lending businesses in India are looking at safer and more efficient services to their customers.
Fraud Prevention & Cybersecurity
Financial lending institutions struggle with managing enormous data, adhering to compliance requirements and enabling security. The service providers are expected to protect their clients against fraudulent activity. Bearing the cost of fraud and the burden of recovery and other associated costs can hamper a financial business’s profitability. Instead of counting on outdated approaches, predicting and preventing fraudulent transactions is the need of the hour. ML-powered sophisticated commercial business management solutions come with the ability of processing high-volume data. Data analytics using machine learning can help firms overcome such challenges by red-flagging unusual user behaviour in case of suspicious activity, and thus minimising the risk of fraud, money laundering, or a data breach.
Businesses can secure their precious financial data using machine learning security solutions. ML-backed intelligent pattern analysis and significant data capabilities make it a better choice than traditional commercial software and security solutions.
Modern financial lending companies seek the help of machine learning technologies to identify risks and set viable premiums on the loans sanctioned. The machine learning technologies make predictions based on historical patterns and current data analysis using intelligent decision-making algorithms. This use of machine learning technologies during loan underwriting can help improve business profitability for financial lending companies.
An after-effect of machine learning and deep learning, automated chatbots have taken over the market by storm. The term “robo-advisor”, which was unheard of a few years back, is slowly replacing the need of human financial advisors in India. These are cheaper and more accessible and serve customers 24×7. While this technology is still at the nascent stage, investors are already looking forward to end-to-end digital services, with no dependence on human agents or physical interaction, and a hands-off, data-driven approach to investment selection.
Fintechs and Machine Learning
The unique ability of a computer program to learn by itself and improve over time creates new use cases for financial sectors. While the financial industry continues to leverage the advanced machine learning and predictive analytics based solutions, the volume of data being generated by the financial players keeps growing day by day. With time protecting the data available in large quantities as well as safeguarding other sensitive assets will be the biggest challenges that machine learning in finance will be expected to tackle. Both banks and other financial institutions strive to streamline businesses and improve financial analysis through ML adoption of machine learning technologies.
Today, as many lending institutions realise the varied virtues of machine learning, they look for solutions that incorporate machine learning technology. At Finezza, we believe in finding novel and creative ways of serving our clients and keeping abreast with global trends. We offer a unique framework designed to help Indian lenders digitise the lending process.
It’s common for digital lenders to execute data extraction from documents, especially KYC. Traditionally, such materials are collected and submitted physically at the lender’s office. They are scanned, uploaded, verified and tagged with the help of the manual workforce. There is scope for error in manual data extraction processes and affects the efficiency at the lending firm. As the need for credit expands in the economy, the demand for data extraction will also increase. The process of manual data extraction is, however, inefficient and leads to a wastage of time in executing the simplest of tasks like extracting Address from an Aadhaar Card. Such lengthy processes during loan origination may take the focus of Feet-on-street (FOS) agents away from engaging the customer. Also, with human intervention, a certain monotony sets in with repetition, and can ultimately lead to negligence.
The Finezza framework comes equipped with a machine learning backed document recognition module. The document recognition module is developed using Deep Learning. The software identifies the type of document from the following eight types:
- Aadhaar Card
- Pan Card
- Cancelled Cheques
- ITR Acknowledgement
- Establishment Photographs
- Balance Sheet
- GST Returns
- Others ( Documents not belonging to the above categories)
Post ML-powered document recognition, our NBFC software also ensures that field detection is executed before Optical Character Recognition (OCR). Here the data extraction module uses Object Detection techniques to extract relevant fields and also crops the image to reduce the area of interest to the text. With Finezza’s machine learning algorithms, the post-processing exercise is just a simple validation that ensures a productive output.
The software, thus, provides an end-to-end system powered with machine learning capabilities that can populate the required fields of a document in 5-10 seconds. It can also identify human biases and correct them during the loan origination process. Lending companies can increase their operational efficiency and reduce customer onboarding time for the business using Finezza, and it’s machine learning capabilities.