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How Big Data Helps Fintech

How Big Data Helps Fintech
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8 min read

Fintech data science is used in retail banking, teaching people about money, online trading, peer-to-peer lending, and other areas. These markets handle a lot of user data in order to give better services and get more profit. Therefore, Big Data is a very hot topic in the world of finance.

What Big Data Means

It’s not a new idea of different uses of Big Data in fintech to better serve customers. For years, many businesses, from small grocery stores to banks in big cities, have used customer data to learn more about their customers. But the “big” in Big Data gives businesses a gold mine of customer information that could change the banking industry.

It’s important to remember that Big Data varies in three characteristics:

  • Volume: Big Data platforms have to handle huge amounts of data, which is something that traditional technologies just can’t do.
  • Quickness: Most businesses need data to be handled in real time.
  • Format: A strong Big Data platform must be able to handle unstructured data like audio, tweets, status updates, and videos.

What Big Data In Fintech Is

In finance, “Big Data” refers to the terabytes of both structured and unstructured data that can be mined to learn more about how customers behave and how to make new rules for them. Data science and fintech combine for risk assessment and predictive analytics. Real-time data is fast, so disruptive fintech and challenger banks can react quickly to a market that is changing.

They might start acting aggressively out of the blue, making it hard for the bigger banks to keep up. Large financial institutions are like powerful diesel tanks, while data-driven fintech is like electric scooters that can get around obstacles and turn tight corners.

Because they have many data science use cases, fintech industry is able to make better decisions and give their customers more personalized services. Fintechs may use Big Data to learn more about their customers instead of making educated guesses or taking the safe route with conservative risk assessments.

Benefits of Big Data In Fintech

It’s clear that combining Big Data and fintech could be very helpful for both businesses and customers in a variety of situations.

  • Fintech data analytics can predict how customers will act, measure risk, and help set strategy. This is why there are so many new technologies for predictive analytics. It can use your data to make scorecards or models for assessing risk to keep an eye on fraud, show targeted ads, and give personalized product suggestions.
  • Big Data analytics lets finance businesses personalize customer service. Banking apps profile customers and provide highly tailored notifications using Big Data. Financial institutions get information from smart devices, wearables, social media, and mobile apps.
  • Data science for fintech also makes it possible for new kinds of verification in finance, like biometric and behavioral verification (e.g., mouse movements and keyboard rhythms).
  • The ability of Big Data to process huge amounts of data in real time is another good thing for the banking industry.
  • Also, Big Data help fintech to build reliable fraud-detection systems, which can help find out about strange financial deals. This is a cutting-edge way to find out about and stop criminal activities.
  • With Big Data analytics, information from many different sources will be put together to make risk assessment better. In particular, it helps you find and plan for risks that could put your business at risk.

Big Data Use Cases

Big Data is actually everywhere in the fintech industry, and it is used in many different ways and for many different goals. Let’s look at a few Big Data use cases in fintech where it makes the most sense.

Online Payments

Big Data and analytics based on machine learning (ML) help with security and finding fraud. But internet payment systems now offer ways to get loans at the point of sale (POS), so people can apply for and get loans while shopping. These systems use ML algorithms and a lot of user data to reduce the number of shopping carts that are left empty and increase conversions.

Insurtech

Even though Big Data affect fintech and insurance companies, many of them don’t use it to make their products. And they often use data and demographics that are out of date. Old-fashioned insurance companies have trouble setting prices that are competitive and miss out on ways to make money. Today, insurance companies use Big Data and machine learning to tailor low-risk policies to the people they want to insure.

Lending

Big Data and artificial intelligence (AI) algorithms are used by microfinance and other lending companies to lower the cost of underwriting credit and make loans available to more people, many of whom have bad credit. The insurance company makes more money, but people pay less for insurance. Fast loans make small and medium-sized businesses more competitive, which helps the economy grow.

Businesses that use POS financing are growing quickly, and so are their sales. Since new companies are starting to compete with Klarna and Affirm, now is a great time to get into the point-of-sale (POS) finance industry. These companies go after poor countries that don’t have many financial services.

Fintech In Real Estate

Companies that work in property finance technology focus on two things. If they want to sell more properties and make more money, they need to use dynamic pricing, keep an eye on the market, include detailed property information in relevant listings, and reduce the number of clients who don’t pay. They focus on getting data from many different places and using analytics to reach the right customers with the right offers at the right time.

Role of data science in fintech is to keep clients as well as get new ones. They could put low-power, low-maintenance IoT devices in rental properties and use cutting-edge mobile technologies to monitor infrastructure around the clock. Real-time information lets the office change rental and maintenance packages to keep customers happy.

Companies can combine, analyze, and present insights from large sets of data in a way that tenants or other stakeholders can buy and use to improve their actions and behaviors. This means that startups can use this financial model to create IoT-based facility management solutions with multiple ways to make money.

Trading and Investing

The future of banking and trading is hurt by cryptocurrencies and crypto-derivatives. As trading systems handle more data and speed of response becomes more important, especially in high-volume trading on volatile markets, the financial industry will continue to rely on data science. Financial Internet trading platforms are now shaped by Big Data analytics and algorithms that learn on their own.

Goldman Sachs, for example, uses ML algorithms and NLP (natural language processing) to evaluate not only quantitative parameters but also media coverage and public opinion. Small businesses may use the same methods for keeping an eye on their brands and doing business.

Big Data Compliance Practice

Analysts who work with Big Data must follow the strictest privacy rules in the world. Recent laws that make it harder to break rules, like the General Data Protection Regulation (GDPR), show that not following the rules could cost a lot of money and hurt your reputation. Data security in fintech keeps your company safe from risks that could cost a lot of money.

Data Security

To follow data privacy laws like the GDPR, you need to protect personal data with the right technical and organizational measures. The first thing you can do to protect your data from being lost or stolen is to encrypt it. Clients, servers, and cluster nodes should also use the Transport Layer Security Protocol (TLS) to protect the data they send and receive.

Encryption could make it slower for Big Data systems to work well. If it’s set up right, it shouldn’t have much of an effect. The security of the data depends on the encryption keys. Last, make sure your Big Data infrastructure is safe. Ensure your access control, identity management, and automatic software updates and patches.

Transparency

Data privacy laws usually require that people be told how their data is used in a clear way. When you collect this kind of information, you have to tell people who you are, what you’re collecting, how long you keep it, as well as the reason you’re doing this.

When a data analysis uses information from another system, you can use that information for many different things. So, this needs to be written in privacy notices and other permissions. Also, these details should be short, easy to understand, and written in English. The field of Big Data analytics is new and vague. Legal and compliance staff should help you write a privacy policy that the public can understand.

Data Minimization

Privacy laws forbid collecting information that isn’t needed. This goes against the belief of Big Data that more data is better. Business analytics rarely require private information. So, it doesn’t make sense to add information to your Big Data platform if you can get all the insights you need without it.

While following rules about data privacy, this may slow down storage and increase costs of fintech software development. If your online privacy policy doesn’t allow it, you should filter out personal data when preparing or taking in data. To ensure accuracy, your data scientists should know about data reductions.

Right of Access

Users can now see, change, or get rid of their personal information because of new privacy laws. Your Big Data infrastructure may include critical consumer data. To respond to a right-of-access request in the right way, compliance teams need to know what personal information you have on a person. Data privacy laws say that these requests must be dealt with quickly and by the due date.

Data lakes get a lot of unstructured data, which makes it hard to do this. Even with this problem, your Big Data analytics solution should make it easy for people whose data you have to look at, change, or delete. So, for regulatory compliance, you need effective inventory management and Big Data archive technologies that can find, understand, and get back personal data.

Bottom Line

Big Data is an important part of the fintech future. This has changed what people expect from all financial services. AI, ML, and Big Data help companies give customers more personalized service. Customer satisfaction is a way to stay ahead of the competition and meet people’s needs. Banks and other financial service providers may now lose customers to fintech startups.

DashDevs can help you create elegant and exquisite analytics modules for your fintech product. Just contact us, and we’ll transform your idea into reality.

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