As far as big data is concerned, its impact on finance firms and Fintechs has been significant in the last couple of years. Studies show that without a shred of doubt. Whether it is online transactions, banking operations, dealing with loads of data, and making purchases using debit cards or credit cards, the finance companies have huge data to manage daily. Then, the exponential growth of complex data is now a problem for the finance sector. There is also an increasing pressure to cut costs of banks, finance firms, online lenders, and Fintechs. This is where big data is playing a pivotal role in shaping the future of these financial companies.
You know that big data analytics is the game-changer in the finance industry and the key to the success of finance companies.
According to an article published on huffpost, all financial transactions will depend on big data in the days to come. That is because finance has huge scope today, which was once a small data discipline but bigger and dynamic now.
There are many developments in big data that the finance companies take note of these days. These changes would shape the finance sector for the better for improved operations and providing exemplary customer services. What are these developments? Well, here are some of the top ones to shape the finance sector in the days to come:
More Focus on risk management
The banks, finance companies, and Fintechs are revamping their organizational risk management infrastructure relying on big data analytics and management of complex information. It helps in boosting transparency, understanding customer issues, and minimizing risks associated with financial data threats or breaches. These little things matter in finance companies for better operations and customer service. Banks and lenders deal with confidential data of customers including bank details and debit or credit card information. Therefore, risk management is imperative to keep data secure. Thanks to big data!
Reaping the maximum benefits out of Consumer Data
The finance companies, online lending companies, and Fintech firms are making the most out of loads of customer data over numerous channels including web, branch, and mobile in order to support unique and new predictive models. It helps in finding customer data and consumer purchasing behavior patterns to improve conversions.
Unlocking data value in operations
The developments in big data storage and meting out structures will help the banks and lending companies to release data value or importance in their operational departments to cut back on business expenditures and explore new arbitrage opportunities.
The requirement to re-engineer ETL to make room for Data expansion
When it comes to centralized data warehouse systems, they will need a conventional extract, transform, and load, i.e. ETL processes to be recreated with the usage of big data structures or frameworks to cope with huge volumes of data.
Increased investment in big data technologies
The International Data Corporation (IDC) anticipated that the demand for data would skyrocket from $130.1 billion to a whopping $203 billion, in 2020 to be precise. If you have worked with lending companies like libertylending.com , you will know the volume of data they deal with and the costs involved in managing such loads of information. It implies that the finance firms and Fintechs would shell out more money on advanced big data technologies, as well as business intelligence and machine learning for future growth and expansion. It shows that big data would prosper and grow in the days to come, which is good news for banks and Fintechs.
As of now, the banks and finance firms can forecast consumer behavior and purchasing habits and personalize financial products and services based on customer lifestyle. These things matter a lot in shaping the finance companies. Thanks to big data technologies.
There is also the possibility of investment in data infrastructure in post-emergent financial markets. In countries such as China, Brazil, and India, both business and economic opportunities are outpacing the US and European nations. That is because major investments are made in local as well as cloud-based platforms and infrastructure.
Embracing of projecting credit risk models and smartphone explosion
When it comes to projecting credit risk models that target huge volumes of data including past purchasing patterns or habits, these being integrated into commercial and customer collections practices to pay more to collection activities than anything else does. It depends on the inclination for felony or payment.
Again, smartphone apps and online-connected devices such as mobiles and tablets are now creating more pressure on the potential of technology infrastructures as well as networks to use, index, and incorporate organized and unorganized information from a variety of sources.
More focus on financial thefts and frauds
As far as Wells Fargo account scandal of 2016 is concerned; it only made us realize how stakes are raised for committing thefts or fraudsevery year. Now, that is a huge challenge for finance companies today. Fines are increasing, and stringent compliance rules have compelled finance firms and online lenders to boost their transaction tracking activities, know your customer protocols, and money laundering identification and the ways to prevent such frauds. When it comes to the regulatory bodies, they will increase their examination and inquiry of financial firm’s business practices and proper investigation of any monetary frauds in the future.
The customers are also expecting announcements to be made by the finance companies concerning some official risk evaluation for banks and Fintechs in the US and other countries worldwide. Management of data and innovative data analytics are primary tools to boost theft detection and money laundering activities. Therefore, waiting for risk data aggregation, risks related to business models, as well as data analytics must be the focus for finance companies and Fintechs.
Customer service will go to the next level with big data technologies together with parallel cloud, data migration, business intelligence (AI), and artificial intelligence. Without them, big data would not have succeeded to gain the prominence today. Moreover, banks and financial firms will deliver more value to their consumers using big data.