Data and Finance
Banks and financial firms deal with the most important facet of our lives: Money. Every day billions and trillions of dollars worth of money in myriads of currencies move from banks to markets and from markets to banks, and it is imperative to keep the record of not only every penny but also who it belongs to. In the times when cash ruled the world, banks mostly used historical data i.e. reports, statements, bills etc, but with the advent of plastic money and online transactions, money itself has become a very important form of data which needs to be managed and safeguarded carefully.
Applications of Data Science in Finance
- Customer Analytics and Personalization: Every bank wants to keep track of its customers. Data Science helps in identifying profitable customers and segmenting them on the basis of their choices in order to provide them with the right plans and benefits. The customer sentiment analysis gives the banks an insight into the behaviors, social interactions and opinions of their clients. While AI tools help to build models that provide better personalization with the customers, Machine learning algorithms help generating reports on financial trends by analyzing previous data.
- Reducing Frauds: Banks and financial firms are always the central targets of fraudulent activities. So, it is necessary for financial institutions to guarantee security for not only themselves but also their customers. Data Science is helping in creating algorithms which can spot anomalies in consumers’ behavior which can lead to fraud. This also helps in investigating stock-market trends.
- Predictive Analysis: Predicting the future is the core application of Data Science. Through social media and other news sources, banks can predict prices, returns, lifetime value of a customer, and also their future moves such as investments or adoption of insurance policies.
- Risk Management: For a financial institution, risks can arise from competitors, defaulters, investors, and even customers. With Machine learning, risks can be monitored and prioritized on the basis of potential losses. AI and Data Science are being used to predict unfavorable and unforeseeable circumstances which may arise in future, and take necessary actions in advance.
Benefits of Becoming a Financial Data Scientist
Application of Data Science in banking and finance is in its early stage, but the potential is endless as more and more people are getting financially literate and making investments. From minimizing risks to detecting frauds, Data Science is all set to reform the entire financial system. Large banking and insurance companies are hiring Data Scientists in large number to not only solve their financial hardships, but also predict the ups and downs of stock-markets and investment trends.
We Can Help You Become a Data Scientist
Having a deep understanding of statistics and business along with technology is essential for becoming a Data Scientist. And that is why the training focuses deeply in these fields. The course consists of practical training of all the tools and algorithms used in Data Science and Machine learning. Multiple assessments and live projects are designed to shape students to work in an industrial environment, and a dedicated placement cell prepares them for getting placed in leading companies.
Source by Shalini M