Application of Machine Learning in Financial Services
One of the latest trends in financial services now is machine learning and their services. It plays a crucial role in managing asserts, evaluating levels of risk, approving loans and calculating credit scores. Machine learning can be defined as part of data science that provides the ability to learn and improve from experience without being programmed. Machine learning as part of artificial intelligence helps develop systems that can access data pools, and the system automatically adjusts its parameters to improve the experience. It was important to note that there are much big data in financial services, and machine learning tends to do justice in drawing insight and predictions. Day to day business transactions and financial transactions like vendors’ and customers’ bills are easily managed through machine learning.
Meanwhile, machine learning can be used in different forms for financial services. They are as follows;
Algorithmic trading
This refers to the use of algorithms in making better decisions in trading. Businesses build some models that can have a predetermined set of instructions on pricing, quantity, and timing for the trade without the traders being actively involved. This has the advantage of analyzing extensive data simultaneously. It helps in making fast decisions, unlike a human being. Again, it is not humans that can make decisions based on emotions and prejudices, so decisions made by machine learning can be very accurate compared to humans.
Portfolio management (Robo-advisors)
These are online applications that are built using machine learning. This usually provides automated financial advice to investors. It has the advantage of being cheaper to use compared to human portfolio managers. When investors enter the investment or saving goal into the system, the system will automatically determine the best investment opportunities with the highest returns. For instance, an investor with 800,000 pounds at retirement can enter his goals into the application. The application can spread them into financial assets like real estate, bonds, stocks, etc. The application optimizes the investor’s goals according to real-time market trends to get the best diversification strategies.
Fraud detection and prevention
Financial sectors like banks lose millions every year to fraud. In fact, cybercrime since ten years ago has multiplied. This is because most financial institutions usually keep their extensive financial data online. Some decades ago, companies were generally easier to bypass, so companies resort to using machine learning to flag and restrain fraudulent financial transactions. This machine learning operates by scanning through large data sets to detect unique activities or anomalies and flags them for further investigations by the security members. This machine learning compares transactions against other data points such as the customer’s accounts history, location, and IP address to determine whether the flagged transaction is parallel to the account holder’s behaviour.
Loan underwriting
In financial institutions like banks and insurance, loans are significant aspects of their businesses. The company does this by examining so much extensive data from their clients. Otherwise, machine learning can be trained to examine all these data and help in the underwriting process. Data science can train the algorithms on how to analyze millions of consumer data to match data records, look for unique exceptions, and decide whether a customer qualifies for a loan or insurance. An algorithm can analyze income, age, credit behaviour, and occupation parameters.
False Positives
Machine learning can help tackle the problem of false positives, which often happens in financial transactions now. False positives can be defined as a false decline, and it happens when merchants or financial institutions wrongly decline legitimate financial transactions request. It usually occurs when there are reasons to suspect fraud. False positives declines are a huge pain point for financial institutions that stand to lose out on customers’ cards.
Risk Management
Most big multinational financial companies depend on accurate market forecasts for the success of their business.AI, and machine learning has been used these days primarily to spot trends and better predict looming risks.
Document Analysis
An improvement in deep learning has transformed image recognition accuracy beyond human capacities. Document analyses are an example of the benefits of machine learning in finance. It has been ascertained that this machine learning system’s speed is great and phenomenal.
Trade settlements
This is the process of transferring securities into a buyer’s account and cash into the seller’s account following the trading stock. Despite the vast majority of trades being settled automatically and with little or no interaction by human beings, some percentage of trades still fall through and need to be settled manually.
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