The world of finance is perceived as being cold, rational, and data-driven. In fact, this is one of the fields where data was cool before it became mainstream (so to speak). Data science for finance professionals has been in play even before anyone started thinking about Machine Learning or Artificial Intelligence.
True, back then, the amount of data was manageable by the human mind with the help of primitive computers. However, the advancements in technology and the speedy improvement in smart algorithms paired with Big Data and Data Science only made the world of finance more interesting and dynamic.
Nowadays, there’s even a special field called Financial Data Science where professionals use Data Science to solve finance-related issues.
This field relies on scientific methods to deliver valuable insight and create predictive models based on structured and unstructured data. Furthermore, Data Science is used for Risk Management & Risk Analysis in order to identify patterns that discourage fraud and irregularities.
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Therefore, the strong bond between data and finance showcases that Data Science as we know it today has some incredible applications in the financial world. Let’s take a quick look at the current applications and the ones to come.
1. Risk Assessment and Management
Every business faces a certain risk factor that can increase or decrease depending on the actions decision-makers take. However, in the world of finance the risks are a bit higher and external factors such as financial crises or an economic downfall can have a permanent negative impact.
That’s why financial companies are more invested in running risk assessments. Luckily, due to easy access to Big Data and processing methods, it’s easy to run a predictive analysis that will help you understand the market fluctuations in the near future.
Financial companies use the data generated during daily transactions, price fluctuations, credit history, and more to understand the trends and mitigate the risks.
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However, in order to do so, a business needs to have at least one data scientist professional who can run the analysis. Also, it takes an entire department to set and maintain the data (to make sure there’s no corruption involved). This can be problematic for small and medium-sized players since they don’t have that kind of budget.
Luckily, nowadays you can outsource your data-related needs to a Data Science consulting firm like RTS Labs. They have the specialists and the tools to provide businesses with accurate analyses and other reports they need.
2. Predictive Customer Analytics
It’s a fact well known that financial services companies are not always the warmest when it comes to customer relationships. Considering the environment, it’s easy to understand why marketing campaigns in this world took time to get more open and inviting towards the public.
But the shocking bit of news is that this openness and flexibility is due to data analytics and Machine Learning algorithms (so yes, more data).
Professionals in finance can now use advanced algorithms to personalize and tailor their services based on the clients’ wishes and preferences. Plus, the use of data-driven marketing campaigns allowed companies working in finance to target the right audience at the right time.
Data Science provides an accurate insight into customer behavior which leads to better business decisions and strategies. We can take insurers as an example here – these companies use Machine Learning to understand their customers and easily identify risky contracts.
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3. Customer Support
Data Science is also used to create intelligent virtual assistants (or chatbots) who can help customers find routine information, directions, and even provide assistance with basic tasks such as account withdrawals, transactions, purchases, and more.
These chatbots are used to provide customers with a stellar experience while the employees get to use their time in a more productive manner.
4. Fraud Detection & Prevention
The financial world is usually under some sort of attacks such as identity theft, credit card fraud, false insurance claims, or tax evasion (to name a few). Back in the days, when financial professionals were limited to rudimentary fraud identification methods, these attacks were often successful, but due to Big Data fraud, it’s easier to identify and predict.
For instance, nowadays users get notified if an algorithm identifies unusual credit card activity. Plus, the financial institution that issued the credit card is notified as well. This way, if it is a fraud, it can be quickly shut down.
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The same goes for insurance claims – before an insurer accepts a claim as valid, they run an algorithm that analyses the user’s past behaviours in order to see if there is a pattern that indicates deceit.
Furthermore, the application of Data Science in finance can help identify money laundering operations and develop the tools to prevent such operations from even taking place. Overall, Data Science has improved fraud detection systems in the financial world and it’s likely the trend will continue.
5. Algorithmic Stock Trading
Stock trading is all about reaction speed and making the right guess. Luckily, due to Data Science algorithms that can run complex mathematical formulas and high-speed computations, financial professionals don’t have to follow their gut when trading.
The access to multiple streams of data (economic data, news, company data, and more) and the fact that these data can be processed almost in real-time allows traders to devise new strategies and trading models. Plus, predictive models take into account traders’ behaviour and future events that may change the stock markets.
Overall, Data Science has made stock trading a more secure business by offering traders access to more sources of information.
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6. Customer Data Management
Due to Big Data and analytics powered by Data Science, companies nowadays can understand the factors that are relevant to profitability when it comes to customers. Plus, information like consumer purchase behaviour and demographics are easily accessible.
Financial professionals can use this merger of data in order to get a holistic view of their market share and understand how to better manage their existing customers. For instance, customers considered to have a higher lifetime value will be managed separately in order to improve retention and loyalty towards the brand.
On the same note, new customers’ data will be managed with a different goal in mind, to increase their desire to learn more about products and options.
It should be clear by now that Data Science, and all its perks, is a field crucial for the world of finance. The methods listed above represent only a sliver of the way financial specialists use data in their daily operations, and yet they are quite impressive.
But Data Science is a valuable field for all businesses, regardless of their domain of activity. Therefore, whether you’re a regular entrepreneur or you hope to become a stockbroker in the near future, it’s important to find ways to enrich your knowledge base and improve your skillset.
Plus, it’s not just about processing Big Data to obtain valuable insight and predictions. Businesses nowadays use advanced algorithms like Machine Learning and Artificial Intelligence to interact with customers and automate routine tasks.
This trend helps entrepreneurs and financial specialists alike to create a better customer experience and improve the efficiency of their marketing campaigns (increased awareness).
Overall, the world of finance is better due to Data Science and similar technical developments. Financial organizations are more open and flexible towards customers and have better fraud-detection mechanisms. It’s a win-win scenario for both clients and businesses.