Unstructured Data Fueling Big Data Analytics - 【2020】
unstructured data in big data

Unstructured Data Fueling Big Data Analytics

Banks and insurance companies have more data than ever. Financial service providers are working intensively with structured data from CRM systems and core applications. However, structured data is only used to a fraction of its full extent. Nevertheless, they offer enormous potential. Artificial intelligence provides the key to this potential.

Decision makers in banks and insurance companies use common query tools for data analysis and therefore prepare information from databases in their CRM systems and central applications. But these only contain structured data. The vast majority of existing data is not included because it is unstructured and dispersed throughout the company. Therefore, it remains useless.

Unstructured Data and Traditional Tools

The attitude toward unstructured data has already changed in some financial service providers. They have implemented early applications to transform unstructured corporate data into effectively usable knowledge. This requires the inclusion of internal documents, e-mails, telephone notes, as well as business news, stock quotes and other market data and combining them with structured information assets.

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When companies begin to integrate unstructured data into their data analysis applications, they face some typical challenges:

  • With traditional enterprise search tools, unstructured data is not easy to find.
  • Without the right tools, searching for unstructured data becomes inefficient and leads to misleading results.
  • Widely used enterprise search tools are not suited to today’s requirements.

Cognitive search prepares unstructured data

Cognitive searches are one remedy for this problem. They identify the user’s intentions and interests and prepare the content of the results in a way that is relevant to decision making. Determining the relationships within the data set found can provide a huge advantage. In addition, companies benefit from the use of intelligent analysis tools combined with machine learning.

Training these AI algorithms with the expertise of business analysts can play a decisive role and help financial service providers to secure competitive advantages.

Unstructured data is essential for banks and insurance companies in determining customer needs and wants. The search for, evaluation and processing of unstructured data provides financial service providers with a complete picture of customer needs. In a situation of intensified competition, this is more necessary than ever to reduce customer turnover and to support lead generation and prioritization.

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The technologies required for cognitive search and access to unstructured data provide methods of artificial intelligence. Some financial service providers are already relying on machine learning in the context of structured data, the next step is to use it in the context of unstructured data.

Three application scenarios illustrate how banks and insurance companies can benefit from the use of AI in the area of unstructured data:

  • Improve customer service: Using AI methods, financial service providers can recognize customer needs at an early stage and identify the potential for cross-selling and up-selling. A complete understanding of customers is the basis for efficient customer service.
  • Strengthen risk management: In banks and insurance companies, assessing business risks is a costly and time-consuming process. AI procedures support companies in automating processes and analyzing unstructured data to diagnose patterns and trends and initiate timely countermeasures.
  • Optimize claims management: Insurance claims processing can also be significantly accelerated with AI automation. Dashboards, for example, allow direct access to all data associated with an insurance claim from various sources and systems. An AI application analyzes the unstructured content of claims reports, extracts the necessary data and prepares it for automated processing in the subsequent claims management process.
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These examples illustrate how banks and insurance companies can use artificial intelligence to optimize their customer-centric business processes. If financial service providers want to strengthen their competitiveness, there is no way to avoid taking advantage of the huge stocks of unstructured data.

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