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Data Science Use Cases in the Fintech Industry


8 min read

Data Science has become a hype technology in the modern world and has created 20 much buzzes in all industries. It combines statistics, mathematics, data analysis, machine learning, and visualization to extract insights from all the big data fintech industry companies obtain. The research results are used for the product and process improvements of the business. Data Science becomes accessible for fintech products because digital services provide rich possibilities for data mining for digital twins companies.

As computer science and technologies were developing, and the possibilities of data extracting were growing, the process of data mining became more practical. The history of Data Science officially started with John Tukey’s book “The Future of Data Analysis,” published in 1962, after scientists began to concentrate on Exploratory Data Analysis and knowledge discovery in the Database. Over time Data Science started to bring new specialties to the market, such as Data Scientist, Data Engineer, Data Architect, Data Administrator, Data Analyst, Data Manager, and Business Intelligence Manager.

Dashdevs helps to improve fintech software development processes. In addition to the basic development services, we also provide Data Science consulting help. As we often get questions from our clients and partners regarding the use of Data Science for business, we’ve decided to write an introductory article to describe the approach, share our best practices and tools, and give common financial data science use cases.

Data Science Project Architecture

The process of Data Science integration with fintech products starts with the analysis of goals and data sources. The former must describe the measurable targets that the business wants to achieve. For a typical fintech product, there is a number of data sources:

  1. User verification services. A fintech account can be created only for a verified user who has passed personal information to the KYC, FinCrime, AT, and AML services. These providers require a photo of the real documents, proof of address, and sometimes a video or selfie.
  2. Card management services. A card can be issued, activated, blocked, closed, or re-issued so that we can get information about all these statuses. Card processing providers can expose information about the places where the card was used.
  3. Payment services. All fintechs products are about the movement of money and balances, so these service providers give fintech data scientists rich information about financial behavior, which becomes the core of the modeling procedure.
  4. Mobile and web app analytics. The application fintech data analytics tools can give information about the usage of in-app features. Mobile and web applications have integrated fintech and data analytics SDKs (software development kits) that are sending structured metadata per the triggers.
  5. Customer support tools. All modern customer support tools provide a wide variety of data concerning user requests, timelines, and resolution of customer problems.
  6. Open data sources. Sometimes we need additional information from government or official statistics that allows us to correlate our results with social and political changes.

All the data is gathered in the data warehouses. We want to put additional attention to three different terms - data lake, data warehouse, and data swamp. The data lakes have unstructured raw data from different data sources. Data warehouses have processed data that is structured and ready for Business Intelligence (BI) processes. However, if the data lake is overloaded with a massive amount of unsorted data, it might become unusable. Such a messy data lake is called a data swamp. That’s why the process of data governance is one of the most critical parts of the Data Science process.

After the data is structured, it is ready for the next processes, such as business intelligence, machine learning (ML) data processing, and modeling processes. Business intelligence helps to get insights from the data.

Modern Data Warehouse

The process and the tool that is used for data structuring is the most crucial decision that is made by Solution Architect and Data Architects. Dashdevs is one of the fintech data analytics companies commonly using Snowflake (one of fintech examples) as a data warehouse for fintech digital ecosystems. Hence we can give you our requirements for the warehouse. These criteria can help you to make the correct decision if you are choosing among several tools.

  1. Cloud solution. We try not to use on-premise solutions due to scalability and maintenance issues. Snowflake’s solution uses Amazon Web Services (AWS) platform for data storage that is called Amazon Simple Storage Service (Amazon S3). This solution is totally secure and easily scalable. On the other hand, Cloud Computing possibilities are much more affordable than hardware. Fortunately, today fintech products are usually cloud-based and this fact simplifies cloud integration.
  2. Extract data from different resources. The data warehouse can receive structured or semi-structured data from different providers and transform it into a usable state. Various tools and service providers send the data in diverse formats, such as XML and JSON.
  3. Ingestion services. Data providers work with different procedures and schedules, so we need a tool to load data continuously. The Snowflake has a serverless computational model Snowpipe that serves these procedures.
  4. Scalability. Data processing is not linear, so we need a scalable solution that supports multi-clustering. Data Engineering processes can have activity and downtime periods.
  5. Integration with Fintech Data Science tools. We work with different data science instruments such as Spark, Python, R, and Anaconda for data analysis and modeling.
  6. Easy DB management. The process of database governance can be complicated. Sometimes we need to clone data or restore it after false actions. All of these actions can be done smoothly by Snowflake.
  7. Manageable sharing. Different teams can do different processing operations with different levels of access to information. For example, the compliance officer needs to have full access to the payment information, including payee and payment details. However, the marketer needs to know only the time and the address of the point of sale.
  8. Cost control. We don’t want to pay for the service if we don’t use it.

The process of data storing and data governance is one of the most crucial tasks for Data Science, so it must be ordered appropriately.

Business Intelligence Tools

The process of finding insights can be done by advanced data analytics in fintech, such as BA, or data analysts in fintech. Fintech data analytics companies are providing similar services to customers, so they need to find their unique positioning in the market. The data from the warehouse or the data lake can contain essential insights.

Consequently, marketers, product owners, and project managers are usual users of BI tools too. When we select the BI tool for the team, we usually pay attention to the following criteria:

  1. Integration with a data warehouse that helps to retrieve data seamlessly.
  2. Clear, user-friendly interface is required for non-tech users with an uncomplicated model and chart creation process.
  3. Easy data management needs access to select, filter, and sort options because Data scientists process the data from different sources and for different timeframes.
  4. Secure access to the tool and manageable access control is required.
  5. The high-performance speed of data processing is a must for any BI tool.
  • Microsoft Power BI is a powerful tool that creates a data-driven culture for businesses. Like all Microsoft products, Power BI gives a strong possibility for visualization tightly integrated with Microsoft Dynamics products. It has an intuitive user experience and extensive possibilities for report creation.
  • Looker helps companies drive better outcomes through smarter data-driven experiences. This solution can be easily integrated with different sources of information. It has high performance and an exciting solution for generating insights. It supports multi-cloud hosting and hybrid environments.
  • Tableau is designed for corporate and personal usage. It has interactive tools for visual analysis that are powered by the patented VizQL technology. Tableau helps to perform content discovery for the data from different sources. The solution can be deployed on-premise, in the cloud, and hosted.

Data Science Use Cases in Fintech

Data Science has become a trend for different fintechs because it can help them solve various business problems quickly. Here are the most frequent fintech use cases:

  1. Fraud detection is the most crucial problem for any financial institution, so they’re constantly looking for anti-fraud tools and different ways of automation in risk management. Different types of frauds try to impersonate, steal, or perform money laundering schemas. Efficient anti-fraud tools must have prevention, protection, and notification systems. The data warehouse receives data on the fly from payment processing systems, passes it through the models, and generates real-time results. Also, Data Science can help to define patterns of fraud collaboration and build schemas and interaction diagrams.
  2. Deep learning of the customer’s performance allows for conducting user segmentation, customer behavior modeling, and real-time and predictive fintech analytics. BI tools allow visualizing the financial activity of the user in the digital bank ecosystem. The user’s financial behavior insights assist with building product strategies for fintech organizations. An additional parameter that can be provided to fintechs by Data Scientists is a customer lifetime value (CLV), which is a prediction of all the benefits that a business can get from the relationship with a customer.
  3. The risk modeling system helps to define if the user is reliable and can be granted access to additional services, higher money credits, and lower rates. Data Scientists can build models based on product usage and open-source information from different sources.
  4. Product improvement strategy can be based on product usage analysis and market information. Data Scientists can build models and predictions of the feature changes in customer behavior and possible reaction to the fintech product changes.
  5. Process improvement can be based on using the Digital twins’ approach, which has been a trend in product development for the last few years. The financial organization or digital bank can track offline operation and customer support processes metrics, analyze them and simulate the changes to evaluate future effects.
  6. Personalized marketing is one of the most powerful tools for fintech product promotion. Data Science gives the possibility to analyze the behavioral patterns of the user and suggest relevant financial products and services.


Fintech, as a young and fast-developing industry, is absorbing all knowledge and approaches that give an additional boost to its products and digital ecosystems. Unlike high-street banks, the architecture of digital banks is more flexible and allows them to integrate with modern services and apply the latest data-mining techniques.

Startups and mature businesses require Data Science consulting services that can empower them to organize processes and improve their products, so don’t hesitate to jump into the Data Science stream now. Feel free to contact us if you have questions about fintech and big data.


What Exactly is a Data Scientist in FinTech?

A FinTech data scientist examines historical data or discovers patterns in provided data to forecast the future based on specific variables. The FinTech data scientist also has extensive subject knowledge and statistical competence.

Is Data Science used in Finance?

Yes, banking institutions utilize data science to know their clients better and avoid fraud. Data science has grown in importance in the financial sector, mostly for enhanced risk management and risk analysis.

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