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Machine Learning for Analyzing Unstructured Data in Finance

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The future of financial services hinges on the ability to extract value from unstructured data. Although banks, financial institutions, and capital markets participants are striving to innovate and adapt to the demands of digital transformation, many are not effectively leveraging the insights that can be gleaned from unstructured data spread across their organizations.

In this blog, we explore the growing trend of using machine learning for data analytics in financial services, the challenges of analyzing unstructured data, such as ensuring data privacy and maintaining ethical standards, and more. Read till the end for access to our whitepaper with tested applications of machine learning for unstructured data in finance. 

What is unstructured data?

In today’s digital age, data is being generated at an unprecedented rate. It is estimated that over 90% of the world’s data was created in the last two years alone. To make use of the vast amount of information in the world, we must organize it in some way, whether by categorizing, assessing, or measuring it. Structured data, which includes labeled or measurable information like names, transaction amounts, and purchase dates, is easily contextualized and ready for analysis since it is typically stored in operational databases or warehouses. On the other hand, unstructured data, also known as “dark data,” lacks a defined data model and is disorganized. When unstructured, this data lacks a clear purpose and is often inaccessible. However, with the appropriate analytics techniques and tools, unstructured data can be transformed into a valuable source of information that can provide in-depth insights into customers, markets, and products.

It is predicted that the total size of unstructured data worldwide will soar to 175 billion terabytes by the year 2025.

Why does unstructured data in Finance matter?

In the case of financial services, unstructured data comprises information from various sources such as customer relationship management systems, customer service records, earnings transcripts, tax documents, and survey responses.

It was reported that roughly 80% of banking data is unstructured, highlighting a significant opportunity for banks of all sizes. This presents two primary applications for the finance industry: combating fraud and enhancing the customer experience.

Structured data such as passwords and login IDs have been used for over 30 years to fight fraud. However, as the risk of fraud rises due to mobile device usage, banks are starting to utilize unstructured data, such as device numbers and geolocation, to detect and prevent fraud by identifying attempts to log in from unfamiliar devices.

Regarding improving customer experience, an emerging trend among financial institutions is blending traditional data with new sources. Combining these new insights with existing information such as transaction data, profile data, and scoring data, can enable banks to gain a more accurate and comprehensive understanding of their customers. This, in turn, can assist banks in creating customized product offerings.

What are the challenges of analyzing unstructured data for financial companies?

While unstructured data can provide valuable insights, analyzing it can be a daunting task for financial services companies. These are some of the most common complexities for them that we have observed from our experience:

  • Despite having access to a large amount of raw, unorganized data that can be highly valuable, they struggle to utilize it effectively due to their compartmentalized and product-oriented structures.

  • A major administrative challenge for them is integrating data from various sources. They must extract insights from data stored on premises, in the cloud, and in hybrid environments, among other things. Furthermore, they need to process unstructured data and transform it into a reliable format that provides transparency about the data’s origin and quality.

  • When searching for valuable insights, finance businesses may struggle to navigate large volumes of data, impeding their ability to create data-driven innovations that are crucial to the digital transformation process.

  • The absence of significant links between data silos has made it more difficult to access information about customers, partners, products, sales channels, and financial performance, thereby reducing data accessibility.

  • The challenge is intensified by the increasing number of data consumption endpoints, business processes, and analytics solutions that necessitate real-time access to data to facilitate decision-making.

  • Unstructured data can be sensitive, and financial services companies must ensure that they handle it with the utmost care to maintain data privacy and security. Therefore, companies must establish robust data governance policies and procedures.

How can unstructured data cause data breach?

Unstructured data poses a significant challenge because it lacks a defined structure and can exist in various formats, making it difficult to classify and manage effectively. This poses a governance issue, particularly for financial services companies that must maintain strict control over their data. Without visibility and control, unstructured data in the cloud can increase the risk of data theft or tampering, and organizations may not even be aware of it.

Cloud solutions like Google Workspace, Slack, and Microsoft Office 365 have become increasingly popular among financial services companies due to their many benefits. However, it is important to properly manage, classify, and sanitize unstructured data when using cloud services. When done correctly, the cloud can be even more secure than on-premises servers. Additionally, cloud solutions offer numerous advantages such as scalability, cost-effectiveness, and accessibility from anywhere in the world. Proper cloud configuration can minimize the risk of data breaches and ensure that sensitive information is protected.

While on-premises data classification tools and data loss prevention (DLP) may be used, they are not designed for a cloud-first world and may not detect unstructured data. These tools are likely to miss out on data and lack the capabilities to identify unstructured data in the first place, leading to an increased risk of data breaches.

To unlock the potential of unstructured data, financial organizations need to effectively use insights. However, many are struggling to do so, despite efforts to innovate and transform digitally.

Download the whitepaper to discover applications of machine learning in risk management, fraud detection, and customer experience, as well as tested machine learning and NLP models for fast ROI. 

Violeta is Digital Marketing Manager at Accedia with years of experience in promoting the value of developing software innovations. She collaborates with IT leaders and key stakeholders to gain insights into businesses latest tech challenges and opportunities.


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