Finance and Business industry always had a disinclination towards advanced technology due to its safety and security concerns. But in reality, the finance industry is most empowered through cutting-edge technologies such as Data science, Artificial Intelligence, Machine Learning etc. Data Science has become extensively important in the Finance Industry which is used for Decision Making, Fraud Detection, Risk Analysis and Management, Automation etc. Data Science can improve financial services through below applications:
1. Fraud Detection
In the financial sector, fraud detection is one of the most useful advantages of data science. To maintain the funds and data of clients secure, most of the financial organizations struggle very hard. Even a single fault in the system paves way for hackers leading fraud worth millions. Real-time and continuous monitoring of data leaving and entering the system and thereby detecting anomaly is possible through data science. Data science predictive models can also detect manipulations and alterations in the system. This can reduce cyber attacks through which damage control can be achieved.
2. Management of Customer Data
Digitizing, storing and searching data which have been received by financial institutions through several sources is a tough task for humans as this leads to increased chances of errors. But through Data science, data processing, data mining, data manipulation can be achieved which provides the ability to squeeze full value of that data. This helps to improve the productivity and efficiency of the organization.
3. Decision Making
Financial investors, managers, advisors and traders regularly view market situations and make decisions based on prediction. These decisions can be wise and smart through Data Science. Navigating data of the past and present, data science authorizes financial advisors to assess the feasibility of trading at a particular time. Through these predictions it is possible to analyze the fluctuating markets and get an idea of moving into a bull or bear market. Financial managers can create a feasible SIP portfolio and can advise you to invest during the right time such that investors can pull funds back and put in more money.
4. Personalized services
Financial institutions which provide personalized recommendations and dedicated services gain more customers. Data science has the capability to provide personalized services to customers such as bringing data together, statistical analysis of Customer data, dedicated questionnaire and tracking, understanding customer behavior through the internet etc.
Risk analysis is a severe factor that data science manages in the finance sector. This includes better understanding of statistics, mathematics, and problem-solving. The risk may arise from various angles such as from share markets, customers, investors, competitors, etc. Through Data science, companies can analyze huge volumes of data which they generate through customer interactions, financial transactions, etc. and train the models to reduce risk factors. Also the creditworthiness of a customer can be judged by analyzing and checking credit history and the data relevant to that customer.
6. Consumer Analytics
By using consumer data and trained predictive modeling, consumer behavior can be analyzed. It is possible to understand customer behavior around using a particular product by tracing the online sentiment of the customer. Though this new product can be introduced in the market.