How Big Data Has Changed Finance

Big data is driving innovation and helping financial institutions generate new revenue streams, increase efficiency, and provide better customer service. The consumption and integration of this data is a key differentiator in the finance sector.


One of the greatest big data challenges financial services companies face is how to take the vast quantity of data generated each day and the data they’ve already captured over the past decade and leverage it to gain a competitive advantage. At the same time, they manage a demanding, diverse customer base that expects a 24/7 omnichannel banking experience.


Financial advisors, investment firms, loan officers, and other professionals in the industry must have immediate access to detailed customer, product, and service information for informed decision-making and to remain in-compliance and competitive.


Because the financial services industry is one of the most data-intensive sectors in the global economy, the full potential of big data should not be underestimated. While some forward-thinking players already use big data techniques like predictive modeling, optimization, and segmentation to maximize customer understanding, many more are lagging behind without a strategy.


Customized, enterprise integration software solutions strengthen and enhance operations by automating business-critical processes, unlocking siloed data, and building a secure foundation for further system improvements.


Ways the financial services sector is embracing big data


There is a multitude of ways big data has changed the finance industry:

  • Data-driven products and services are now conceptualized and designed using insights gained from various data streams.
    • Algorithms analyze massive amounts of structured and unstructured data to identify investment opportunities.
    • Car and home insurance products are combined with IoT technology to improve pricing and protection.
    • Wealth management advice has become truly personalized by identifying each customer’s unique goals and situation.
  • Risk management has improved through enhanced real-time insights into customer behaviors. Identity theft and credit card fraud detection and prevention, liquidity, and credit-risk/legal claims management are strengthened to reduce risk exposure.
  • Sales and marketing strategies in acquisition, activation, and cultivation or relationship management are focused on:
    • Identifying potential customers
    • Maximizing sales opportunities – Cross and upselling opportunities are identified. As the sector adopts a customer-centric business approach, it’s using big data insights to produce more focused and in-depth interactions with customers.

Targeting the right audiences with personalized messaging on multiple channels


The benefits big data brings to the financial industry are clear. But data silos, the sheer amount of available data, and a reluctance to make needed cultural shifts pose significant challenges. To compete, enterprises that haven’t embraced all big data offers (usually for cost or legacy reasons) must begin to look at innovation, data, systems integration, and regulatory compliance as an investment rather than an expense.


Applications of big data in finance


Many financial services providers may remain resistant to change, but make no mistake, big data is here to stay. The IDC reports more companies than ever are purchasing big data solutions. The banking industry is one of the top 5 biggest drivers of this growth; big data offers a variety of solutions for lending, risk, scoring, fraud, and more.


Each organization’s data needs are different. Selecting a cloud data platform that’s both flexible and scalable is key to collecting as much usable data as possible while keeping it secure and accessible at the same time. AWS, Azure, and Google Cloud are the 3 largest Cloud providers used by the financial industry to support technology platforms:

  • DemystData uses a centralized dashboard that lets financial institutions access data to
    • Automate processes like verification
    • Efficiently profile customers
    • Enhance compliance
    • Flag potential fraud
  • San Francisco-based Flowcast offers an enterprise-scale machine-learning platform to help banks and other lenders make better credit decisions. With this AI-powered solution, lenders gain better insights into ecosystem risks while delivering better service to new and existing customers.
  • To combat digital fraud, Sift Science uses machine-learning to ensure digital transactions are secure for consumers and merchants. Customer data is analyzed for patterns of risky behavior so financial institutions can stop fraud before it occurs.

Finally, customer relationship management (CRM) software helps financial services providers build new relationships and increase value through sales and marketing tools. At least half of financial service businesses use a CRM system to improve everything from call center metrics to virtual services.


The top industry-specific CRM platforms include BNTouch and Pulse for mortgage lenders. iPipeline is an end-to-end solution that’s designed to accelerate and simplify sales, customer support, and compliance operations.


The ability to analyze diverse sets of data offered by these and other platforms has forever changed how the financial industry operates. Innovative organizations are better equipped to make informed decisions that foster growth while providing customers with customized solutions designed to secure their financial standing today and tomorrow.

Topics: Big data, Finance, Big data in finance