Banking and finance are extremely complex sectors to handle. Hundreds of regulations, risk factors of managing finance, and a huge demographic of customers. These factors make it really difficult for banks and finance companies to provide satisfactory services.
Hence, it would be obvious to say that banking and finance services contain a big amount of data. However, the smartness of a company lies in utilizing that data to improve processes. Every customer interaction, transaction, and other processes create electronic data to record, store and utilize. Smart companies incorporate advanced technologies and get the maximum out of their data.
Applications of data in banking and finance
In today’s scenario, finance and banking services don’t like to wait to conduct data analysis and get results. Most of the evaluation takes place in a real-time, making decision-making quicker and accurate for these services.
There are a variety of processes where data is used to attain precision:
1. Segmentation of customers
Banks and finance companies target a very broad group of customers. However, every customer deserves different approach due to his or her behavior. Customer segmentation is all about targeting customers according to their behavior.
With the rise of data, banks have understood that product-centric marketing is not effective. Finance companies have played a valuable role in categorizing customers too.
Analyzing huge amounts of data, companies find out valuable information related to transaction demographics, personal conditioning, and other factors. Then, it becomes extremely easy for services to create groups of customers according to their behavior.
2. Risk assessment
Risk assessment is involved in every sector. However, finance and banking companies need to attain a clear picture of potential risk in order to avoid hidden financial dangers.
Information is power when it comes to risk management. The more you know, the better. Advancement in the data analytics has given extreme powers to banks. Data technologies allow services to gather loan history, credit card details, and other information.
Combining data from multiple databases, services make risk evaluation accurate and effective with data. Multiple data sets are tested and tracked on an ongoing basis to keep finance companies on the right track.
3. Personalization in marketing
Personalization in marketing becomes possible with a hyper-segmentation of customers. Companies try to categorize customers into more specific segments.
Mostly, finance and bank services target individuals according to their buying nature. Data comes handy in this process as well. Finding browsing habits of customers helps services understand what they are looking for. The collected data is then converted into strategies and analyzed to meet company goals.
There are many different forms of data that help in personalizing services. Companies collect data through social media profiles in order to know the likes and dislikes of consumers and their sentiments. Technologies such as machine learning and NLP make sentiment analysis of collected data easier.
4. Predictive fraud analysis
Detecting a fraud is probably the most difficult job finance and bank companies have to conduct. Having millions of customers with all kinds of services makes fraud detection really hectic.
But, not when predictive analysis is employed with machine learning.
Integrating machine learning with data allows services to track every activity in real-time. Machines analyze daily activities of a bank and keep a track of the activities. Any fraudulent activities are detected immediately. In fact, banks can automatically take actions such as blacklisting a card, blocking an account or any other valid action.
5. Attaining compliance
Regular audits are necessary for financial and banking services in order to ensure compliance. Auditing information, activities, finance and other factors require data and evaluation technologies.
Maintaining a high standard of compliance, banks and finance companies use data analysis to audit security and privacy levels in their company. Potential regulatory issues become visible with comprehensive data analysis. Hence, services obtain an opportunity to tackle those regulatory issues before a crisis strikes.
Finance companies and banks are leveraging data to improve internal and external business functions. Data incorporation has become a necessity in terms of customers, compliance, and business as well.
Bank and finance companies that are using data in unique ways
An industry completely based on money, banks and finance companies have to rely on data. It is not just about knowing more. The industry saves hundreds of hours with data analysis and machine learning.
Here are a few companies using data in a unique manner to improve processes.
This financial services provider holds a customer base of more than 200 million in over 160 countries.
Applying a comprehensive data-driven approach, Citibank gathers data and segments it into a granular level. Then, machine learning is used to understand the potential use of data in customer acquisition and retention. Data tracking is used in finding transaction records, which minimizes incorrect transaction issues. A predictive model is created by algorithms that allow authorities to modify processes before an error occurs.
The credit score of a person helps in assessing his or her risk profile. Financial services use credit scores to decide the eligibility of a loan seeker. However, there are thousands of seekers that have no credit score.
Using data and machine learning, Kreditech is resolving that problem. They collect data from a variety of points and conduct an algorithm based analysis to find eligibility of a person. It doesn’t take more than a few minutes for algorithms to establish a credit score.
A lender has to decide which borrower is perfect to lend money. Finance companies evaluate hundreds of factors to understand about borrowers. However, lack of proper information and time-taking evaluation presents the risk of losing customers.
To resolve this problem, ZestFinance has found a reliable solution with data. Integrating machine learning in borrower data analysis allows this company to collect and analyze data from thousands of points. This way, lenders obtain quality information without losing opportunities.
Hidden risks stop investors from making profitable decisions. Most investors limit their investments due to the lack of risk assessment. However, an investment looks like a risk only until you have no clue what you are doing. Once you have all the potential outcomes visible, investing become much more comfortable.
PeerIQ is making this happen for investors. They collect data and conduct a predictive analysis to provide information that is useful for investment decisions. Gaining helpful insights allows investors to get a clear picture of their investments in advance. Hence, they can put their money in the right products.
Another company that is using data in credit analysis is Tala. However, they employ a unique approach to credit evaluation. Tala uses mobile data of hundreds of thousands of users and creates useful insights related to their credit.
As mobile phones are used by almost every person, Tala is able to collect a wide range of data and analyze to find perfect borrowers. Data categorizes people into two major categories, but there are hundreds of factors that work on data. Eventually, the company obtains a list of people who fit the criteria of becoming a borrower.
When thinking about auditing, finance companies worry about losing hundreds of hours. A great amount of manpower is required for manual auditing, even when data is available. But AppZen has resolved this problem. This finance service has brought automation in auditing with machine learning. A huge amount of data allows machine learning algorithms to automatically audit business functions in a real-time.
Investing in machine learning data auditing has allowed this company to reduce costs to a great extent. AppZen saves almost 50% of their general costs with automated data auditing.
Suppliers always look for reliable financing options that are affordable. Flowcast is making this possible with their API. A huge data collection and organized insights allow suppliers to find financing solutions that are most suitable. Hence, a difficult task starts seeming extremely simple with Flowcast.
Data and machine learning are two pillars holding the future of banking and financial services. Many companies have understood this and started moving forward, and others are planning to do so. This means that investments in data-driven processes are going to increase in the finance sector. Companies will hire professionals who are technically capable of managing and using data. All in all, the future of banking sector holds a variety of data-driven processes, which will revolutionize the industry furthermore.
Hopefully, this shift towards datafication will keep on growing and improving customer experience, compliance, fraud detection and other aspects of this sector.