Data science has improved financial services by speeding up processes that would normally take a long time.
For example, SafeGraph helped one of its financial services clients provide data to assess whether customers will be going to the bank during the COVID-19 pandemic.
This helped the client make an accurate assessment of how the pandemic will affect a particular bank and helped the bank make sound business decisions going forward.
In this article, we’ll look at other ways big data can be used in financial services. We will also suggest several ways to use it depending on your niche or your needs. We’ll consider:
- 4 benefits of using data science in the financial industry
- 7 Best Uses for Big Data in Financial Services
It is important to look at the top 7 big data use cases in financial services to understand the concrete impact of these technological changes on the banking world. And beyond these big data use cases, the financial sector has benefited from advances in data science that may not be immediately obvious.
What is big data in finance?
Big data in finance is large, diverse (structured and unstructured) and complex datasets that can be used to solve long-standing business problems for financial and banking companies around the world.
The term is no longer restricted to the field of technology, but is now considered a business imperative. Financial services companies are increasingly using it to transform their processes, their organizations and the entire industry.
How big data is revolutionizing finance
The exponential growth of technology and the increase in the amount of data generated are fundamentally changing the way industries and individual businesses operate.
The financial services sector is by nature considered one of the data-rich sectors, providing a unique opportunity to process, analyze and use data in useful ways.
Traditionally, numbers were handled by humans, and decisions were made based on inferences drawn from calculated risks and trends.
Recently, however, such functionality has been usurped by computers. As a result, the big data technology market in finance has huge potential and is one of the most promising.
Real-time stock market analysis
Big data is completely changing the way stock markets around the world function and how investors make their investment decisions.
Machine learning—the practice of using computer algorithms to find patterns in vast amounts of data—allows computers to make accurate predictions and make human-like decisions when fed data, making trades at high speed and frequency.
The business archetype tracks stock market trends in real time. It includes the best possible prices, allowing analysts to make smarter decisions and reduce manual errors due to behavioral factors and biases.
Thus, when combined with big data, algorithmic trading provides traders with highly optimized information to maximize their portfolio returns.
Big data analytics in financial models
Big data analytics provides an exciting opportunity to improve predictive modeling to better understand rates of return and investment outcomes.
Access to big data and improved understanding of algorithms provide more accurate forecasts and the ability to effectively mitigate the risks inherent in financial trading.
Customer Analytics
Today, customers are at the center of the business around which data analytics, operations, technology and systems revolve.
As such, big data initiatives by companies operating in the banking and financial markets are focusing on customer analytics to improve the customer experience.
Companies are trying to understand the needs and preferences of customers in order to anticipate their behavior in the future, generate leads, take advantage of new channels and technologies, improve their products and increase customer satisfaction.
Thus, by effectively developing meaningful relationships with their customers and improving their ability to anticipate customer preferences, financial market organizations can provide new customer-focused products and services to quickly seize market opportunities.
For example, Oversea-Chinese Banking Corporation (OCBC) analyzed vast amounts of historical customer data to determine individual customer preferences and develop an event-driven marketing strategy.
The strategy focused on a high volume of coordinated, personalized marketing communications across multiple channels, including email, text messages, ATMs, call centers, and more.
Risk management and fraud detection
Financial institutions are using big data to reduce operational risk and fight fraud, while greatly alleviating information asymmetries and achieving regulatory and compliance goals.
Banks can access data in real time, which can be useful for detecting fraudulent activity.
For example, if two transactions are made through the same credit card within a short period of time in different cities, the bank can immediately notify the cardholder of a security risk and even block such transactions.
Also, in the case of insurance, the insurance company may have access to social media data, past claims, criminal records, telephone conversations, etc., in addition to claim details, when processing a claim. If it finds anything suspicious, it can flag the claim for further investigation.
In order to effectively combat fraud, Alibaba has established a fraud risk monitoring and management system based on the processing of large.
Big data challenges facing the banking and finance industry
Compliance
Financial institutions must comply with the strict Fundamental Review of the Trading Book (FRTB) regulatory requirements developed by the Basel Committee on Banking Supervision (BCBS), which govern access to critical data and require expedited reporting.
Data privacy
Data privacy is another major issue associated with the adoption of cloud computing technologies. Companies worry about hosting sensitive information in the cloud, and while some have set up private cloud networks, such projects can be costly.
Data warehouses
The inability to link data across departments and organizational repositories is now considered a major business intelligence problem, leading to complex analytics and hampering big data initiatives.
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