The advanced computing and programming technologies of the twenty-first century have brought forth artificial intelligence(AI) and machine learning applications from the pages of fiction. Their integration has led to the development of robust products with the capability of automating various processes and solving complex problems. The machine learning models can simply learn from experience and do not require explicit programming.
Machine learning applications in the finance industry are numerous, as it deals with troves of data, including transactions, customer data, bills, money transfers, and so on. Many organizations in the sector are already exploring the capabilities of AI and machine learning technology to streamline their processes and gain a competitive edge in the market. The highly regulated finance industry is facing the imperative need to reinvent itself, especially in the wake of fintech, digital and payment banks, government regulations, and consumer behavior change. The traditional banking institutions are exploring, experimenting, and investing in the use cases of machine learning.
Let’s learn about how machine learning can solve finance industry problems.
Machine Learning Applications in Finance
Improving Customer Experience
Customer service is one of the primary factors which decide the level of engagement of consumers in financial institutions. In the wake of competitiveness in the market, organizations should provide quick and accurate information to its customers. To fulfill this, the finance industry need not look beyond the robust capabilities of machine learning. Chatbots leveraging the self-learning ability of machine learning can be used to facilitate comprehensive, self-help solutions, which can not only enhance customer engagement but also reduce the workload of call centers. Organizations can also offer personalized financial advice through machine learning applications, intelligent systems to monitor income, essential recurring expenses, spending habits, and so on.
For instance, the State Bank of India (SBI) leverages SIA, an AI-powered chatbot to address customer enquiries. The solution was developed by Payjo and has responded to over a million queries from thousands of customers. SIA can process over 10,000 enquiries per second.
The incorporation of machine learning applications enables organizations to take control of their risk management processes by identifying patterns and trends in the market and comparing it with the loan applications and financial reports of clients to forecast their creditworthiness. It also allows commercial organizations to prevent financial crimes and form effective strategies to avert financial crises.
The increase in online transactions has fueled credit card fraud over the years. Machine learning has enabled the finance industry to develop effective shields against financial fraud and money laundering. By incorporating the technology, organizations are deploying robust fraud detection systems to detect anomalies in client behavior, location, and buying habits. The complex machine learning algorithms can assess the various interactions based on multiple variables and patterns, which are continuously updated over time.
Financial organizations can significantly bolster their marketing strategies by incorporating machine learning technology. The advanced algorithms can be used to track patterns in the vast financial data, thus generating predictive analytics to inform their decisions and predict marketing trends. Organizations can also leverage machine learning platforms to analyze web activity, mobile app usage, ad campaign response, and so on, thus strengthening their predictive and marketing capabilities.
Robotic process automation (RPA) backed by machine learning will not only enable industry leaders in the financial sector to cut operation costs, but also to boost their productivity. Similarly, the incorporation of natural language processing (NLP) has made possible the automation of mundane, time-consuming tasks, significantly reducing work hours. The machine learning solutions can be used to verify data and generate reports according to the given parameters, review documents, and extract information from forms. Automating repetitive tasks will also enable financial organizations to eliminate human errors and divert their workforce toward processes that require human involvement. Machine learning models can also be used to automate back-office and client-facing processes, empowering them to interpret documents, analyze data, and execute intelligent responses.
Case in point, ICICI Bank recently launched an ML-powered software robotics platform to automate multiple operations. It can process over a million transactions in a working day. The platform is designed to capture and interpret information from various systems, identify patterns, run business processes across multiple applications, data entry, validation, automatic formatting, multi-format message creation, text mining, workflow acceleration, reconciliation, exchange rate processing, and so on.
The rising number of data breaches has brought data security to the forefront of concerns in the financial sector. Not many technologies can match the capabilities of machine learning when it comes to data security. To counter the advanced and sophisticated attacks of cybercriminals, it is crucial for organizations to keep their safety features up to date with the latest technology. Machine learning security solutions utilize intelligent pattern analysis and big data capabilities to secure financial data in organizations.
Financial organizations can automate the trading process by leveraging algorithmic trading, which empowers the execution of trades according to predefined criteria, buying or selling stocks when the price reaches a specific level. The incorporation of machine learning models will not only enable them to streamline the process, but also to make it intelligent by analyzing historical market behavior, identify patterns, determine optimal strategies, and predict trades.
Organizations often fail to extract value from machine learning technology due to a variety of reasons, including lack of knowledge and skill. Developing the technology models is not only expensive but also requires troves of updated, quality data. Addressing the shortage of data scientists and skilled engineers is required before expecting to harness the full potential of machine learning technology. With more and more technology professionals upskilling and moving to machine learning, we can soon expect a change in the demand-supply gap for ML engineers and even more widespread adoption of machine learning in finance and other industries.
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