Data science has the power to mitigate risks in the finance industry, the one important aspect which makes data science in finance so much essential. Professionals gearing up for a career in data science will surely benefit from knowing the underlying phenomena and processes of using data science in finance. After all, this opens up new avenues to explore in your career.
Unlike other domains, the use of data science in finance is highly strategic.
During an economic downturn, data science acts more like a rescuer – enabling organizations to figure out how to cut down costs and increase efficiency. It helps financial organizations sail through recessions and other negative economic events for longer. This is probably one of the most crucial applications of data science in the finance domain. Other applications include:
1. Risk Analytics
After the 2007-2008 financial crisis, risk analytics have become a hot topic in the finance domain. Risk analytics leverages data science to let a company know about the potential risks that can jeopardize its existence. It includes facets such as:
• Understanding competitors
• Staying updated with environmental changes
• Pitfalls when investing a huge amount of money
• Challenges in rolling out new products, etc.
2. Consumer Analytics
Financial companies have a lot of products that they want to upsell. For instance, a bank will always try to push to sell a credit card. To do this, they need accurate data about their customers. For instance, trying to sell a loan to someone who is already repaying a loan is useless. Rather, it makes more sense to push a consumer who visited the loan details page some time back and has enough capital in their account.
3. Fraud Detection
The size of financial companies has grown over the years and so is the number of transactions that take place. While technology has eased our lives, it has also breached privacy and has increased the chances of being a victim of fraud and scams. Financial institutions take help from data science to develop models that can identify the fraudulent transaction.
4. Algorithm Trading
One of the most difficult and highly lucrative applications of data science in the financial industry is the use of ML and AI-based models to predict stock prices. This helps hedge funds and other investment firms to make investments and gain large profits. This is a highly difficult application of data science and requires extremely sophisticated modelling frameworks.
5. Better Fund Allocation
Like any other organization, financial institutions also need to keep a track of the budget requirement of the various departments. Data Science can help in allocating this budget by predicting future needs based on the past record of the department’s spending. This can make money allocation more efficient and less prone to leakages.
6. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐞𝐫𝐯𝐢𝐜𝐞
Data science can help solve customer service problems by automating traditional customer support through chat boxes where customers can instantly provide specific information without having to wait their turn to speak to an agent. The data science-based concepts of natural language processing and deep learning can help here.
7. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Consumer information such as background, interests, likes and dislikes, and financial stability helps us tailor conversations and experiences to consumers. This keeps the conversation on point and consumers feel valued. Recommendation engines play a key role here, helping financial institutions make more informed decisions when dealing with customers.
8. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Today, however, data from social media, blogs, news, and other platforms are often recorded in an unstructured way. This allows financial institutions to adopt aspects of data science such as big data to store data and use various data science-based techniques to transform this structured, semi-structured, and unstructured data into I came to understand These data should eventually be combined to produce a single better image. of the world around them.
9. 𝐂𝐫𝐞𝐝𝐢𝐭 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧
Traditionally, an applicant’s creditworthiness is assessed by a credit score calculated by insurance companies and rating agencies based on past repayment history. However, with data science, the loan origination process can be much more informed. Not just credit scores, but demographic details and other digital footprints (which include social media, reviews, or other public data associated with the customer) to build predictive models to make intelligent underwriting decisions. customer creditworthiness.
10. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠
Statistical or predictive analysis is often performed to understand how an asset will perform in the future. This allows financial institutions to make critical decisions and reduce the chance of error. But the world is constantly evolving, making building models very important models that not only perform well but adapt quickly when given new information. Certain decision problems required more than statistical analysis, such as decisions about buying stocks that only machine learning and artificial intelligence models can help with.
