The financial industry has always been a highly competitive sector. Considering how disruptive technologies like Big Data have reached their maturation, big data can be made a beneficial part of the financial industry. Businesses can harvest big data for security, personalization, and investment decisions.
Big Data is bringing forth new datasets that can help understand customer behavior and improve the area of predictive analysis. With this data-driven approach, let’s take a look at how Big Data is transforming the financial industry.
Enhanced Product Diversity
As stated previously, Big Data is now highlighting new datasets that are a powerful medium to understand the customer psyche and accordingly offer them new and improved financial services.
For example, companies now operate fintech Robo-advisors that offer holistic advice regarding digital investments. Given that these Robo-advisors make use of Big Data to gather insight on customer spending patterns and other parameters for personalization, the advice tendered will also be extremely relevant for the customer. Similarly, other services like loan availability, customer risk analysis, etc. can be included in the list of financial products.
Investors can effectively tap into the potential offered by big data to analyze market trends and make smarter investments. Several companies have cutting-edge predictive systems in place, which can not only understand large volumes of data but also interpret them to offer informed investment decisions.
With AI-powered trading, investors can increase the profitability of their investments. As a result, the area of market investments is no longer limited to veterans or seasoned investors but also extends to newbies who wish to try their hand at capitalizing on market gains.
In the financial industry, certain services are more vulnerable to security lapses and frauds. Thus, big data can play a vital role in plugging these gaps and keeping customers safer. Lending institutions and banks are making use of a combination of machine learning and big data (clearinghouse.org) to automate their security. Further, it keeps them two steps ahead of any miscreant who looks at exploiting security loopholes, especially in outdated systems.
Location intelligence keeps track of where the customer is using the financial service. It also monitors the kind of products or services that they normally purchase and the number of transactions per cycle. With this information, big data can monitor and highlight deviations from the regular purchase patterns to alert and protect users from fraud.
Fewer Manual Processes
Big data will usher with it the era of artificial intelligence and machine learning. As a result, manual and repetitive processes like documentation, looking up customer history, etc. can be automated through algorithms. Furthermore, it also decreases the response time while also abiding by the prevailing regulatory structure.
While cutting down manual processes does offer a customer-centric approach, it is feared that it will jeopardize the job security of individuals involved in these manual processes. This fear is exacerbated by the fact that technologies are more efficient, more accurate, and much cheaper. However, the displaced human resource can be utilized in new and diversified positions after thorough training.
Personalized services are one of the key takeaways of big data-assisted financial services. On the basis of the customer’s spending habits, financial institutions can offer personalized recommendations and upsell products that will meet their needs. With this value-added approach, the companies can develop customer loyalty across all verticals and enjoy a strong consumer presence.
Accurate Risk Analysis
Previously, financial services like loans were based on one or two factors like credit score, debt-to-income ratios, etc. However, Big Data has diversified these datasets and introduced several variables that can offer a more concrete and individualistic risk assessment of the individual.
Machine learning factors in economic conditions, business capital, customer segmentation, etc. in an unbiased manner to identify risky investments or defaulters.
While, on paper, Big Data may appear like the ultimate solution for all financial institutions, it does bring with it certain challenges. These obstacles may be company-specific and include:
Big Data is characterized by three “V”s: Volume, Velocity, and Variety. Essentially, it means that Big Data technologies handle vast quantities of data in a static and real-time environment while supporting multiple data types. Financial companies are either unable to compute such volumes of data or cannot access this from multiple channels. Moreover, data silos make it difficult to integrate all the collected Big Data.
As a result, they are unable to tap into the full potential of Big Data.
Accuracy and Quality
Diluted and inaccurate data is of no apparent use. Companies have to make use of reliable data to capitalize on the opportunity. When it comes to the financial industry, it becomes even more imperative to seek accurate and reliable data, which is a major challenge faced by several institutions.
Security and Integrity
Banking and financial institutions shall have to maintain the highest standards of safety and security when storing sensitive personal data of their customers. Any security breach or possible threat could result in a severe loss of trust. Some companies may not be prepared to offer this level of data security.
In addition to online regulations, there are several banking regulations regarding data security, consumer privacy, reporting, and transparency. Adhering to these regulations while also keeping to digital safety can be a difficult task to balance.
In the years to come, it is clear that Big Data will revolutionize how we perceive the financial industry. Big data will give companies an insight into customer behavior and profile the individual into certain types. Resultantly, this data can be of extreme value to businesses to further their business and establish a loyal customer base.
It is only a matter of time until Big Data emerges as a second currency in the financial industry.
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