Financial industry has been catching up on the Big Data revolution recently, and the key players have been bulding infrastructures and changing their environments to make sure they're technologically well-suited for it. Mid-sized and small-sized firms (regional banks, brokerages, asset management firms, algorithmic trading firms etc.) are moving faster in adapting these technologies, simply because they're more nimble than their larger competitors.
Aside from using analytics capabilities in large scales of data, the financial services industry has to also be aware of the regulatory and compliance issues involving data. Increasingly, these aspects have been starting to get integrated into big data platforms, and is expected to be the norm in the near future.
With the help of Experfy's courses, you can gain extensive knowledge and skills in the most important topics involving big data in the financial services industry. These include:
- Fraud Detection: According to the PwC Global Economic Crime survey of 2016, 36% of the institutions have experienced economic crime in the last 24 months. This number is continuing to grow, and everyday novel methods of preventing economic crime have to be invented. With the increase in the sources of human generated data, and advances in machine learning techniques, it is now possible to sift through a massive amount of data using algorithms that improve their parameters based on training data. This leads to more accurate predictive capabilities.
- Actuarial Science: Assessing the amount of risk involved in giving out loans and deciding on insurance premiums is a core part of the banking and insurance sectors. Experfy's courses will give you a deep understanding of how to approach problems involving risk, and most importantly, they will teach you what the limitations of mathematical models are and where judgement comes into play.
- CAT Events Modeling: Predicting the potential losses in the event of a hurricane or an earthquake plays a very important role in the insurance industry. By understanding how nature plays a role in determining insurance premiums, you can come up with more accurate insurance premium prices for your customers.
- Credit Scoring: The science of credit scoring has existed long before the big data revolution, but it's undergoing a very rapid change with the advent of big data technologies. Lenders can now aggregate different sources of data to better assess the probability of a customer paying their debt in time with the use of scientific models such as logistic regression or decision trees.
- Apps Utilizing Big Data: Fintech startups provide their customers a front-end link to vast amounts of data from different sources. With the help of big data, companies can now utilize these apps to do various things like providing fine-grained insurance policies, more accurate credit scoring, and automated advisory services.
- Algorithmic Trading: Algorithms can trade at a higher speed and frequency than humans can, and with the help of big data, they are becoming more accurate every day. Since they're now able to incorporate large amounts of data, they can make less risky investments with higher returns.
Whether you're a manager, an industry professional or a recent graduate looking for a career in the financial services industry, Experfy's demo-intensive courses can help you get up speed with modern data-driven methods and help you gain the skill set to apply them in real life problems.
According to Indeed, the average salary for a financial services industry professional with data science skills is $115,000/year.