Why Unstructured Data is Important in Financial Services?

Introduction

The sector of the economy that deals with financial services are now experiencing a period of profound change. Organizations that deal in financial technology are at the forefront of this transition. These companies are the ones who are providing innovations such as hyper-personalization by using capabilities in Artificial Intelligence (AI), conversational interfaces, blockchain, and crowdsourcing.

The financial services sector is becoming more open to innovation due to the disruptions that have occurred. However, compliance with rules and laws is the other side that must be considered with innovation. As a result, financial institutions are making significant investments to enhance their Unstructured Data capabilities to make sense of transactional and business data and to comply with laws.


Utilizing Data That is Not Structured

Financial organizations may get undisclosed actionable insights from unstructured data, such as consumer preferences, unmet customer demands, and market and process gaps. Banks, for instance, may utilize this information to intensify client experiences, allow new goods and services, and conceive more imaginative operating models. However, interpreting unstructured data is very difficult since it consists almost entirely of language and often lacks standards.

It delivers in a variety of ways and draws from several different sources. However, the conventional methods of data analysis are ineffective because unstructured data are not legible by machines, which is an essential need for research utilizing AI and machine learning.


Finding Solutions to the Problems Caused by Unstructured Data in Financial Institutions

A combination of factors, including the highly regulated nature of the financial services business and the rise of digital disruption inside the industry, has resulted in an exponential growth in the amount of unstructured data. As a result, it is necessary to link the data from various sources, such as passports, pay stubs, application forms, leases, loans, and mortgage portfolios, with the data already accessible from the organization to provide individualized client experiences.

The following are some examples of practical solutions offered by Strive to companies in the financial services industry:

Banks

Automating document digitalization and processing as part of mortgage and loan origination, client onboarding, and KYC procedures.

Developing structured data sets to ease the process of reconciling the Mortgage System of Record with the System of Origination.

Increasing the efficiency of the KYC procedures that are already in place by integrating data obtained from unstructured, internal, and external sources, such as phone records, court documents, financial filings, news, and social media.

Consolidating various watch lists to monitor anti-money laundering activities and lowering the number of false positives generated by transaction monitoring.

Capital Markets

Building alternative data sets from publicly available records such as job vacancies, management changes, social media sentiment, product reviews, patent analyses, and court filings to produce alpha by finding performance signals.

Monitoring customer call logs, transcripts, and complaints to ensure compliance with applicable legislation and bank policy regarding sales practices.

Governance, Environmental, and Social Concerns (ESG)

The process of collecting ESG reference data and producing benchmark ratings from unstructured sources like annual reports, sustainability reports, and corporate social responsibility reports.

Locating and monitoring ESG-related disputes and events that move the market using news wires and press releases as sources of information.

Insurance

Automating document categorization and extraction improves client onboarding and makes the know-your-customer (KYC) process more efficient during underwriting.

For underwriters to assess risks, the process of extracting entities and essential data from forms and other external sources is necessary.

Utilizing both internal and external communications to glean feedback from customers to provide more comprehensive 360-degree perspectives of those customers assisting with the characterization of damage and the calculation of potential compensation in connection with the settlement of Property and Casualty (P&C) claims.

Core Benefits of the SDP

Financial organizations such as banks, investment businesses, and mortgage firms may utilize SDP to swiftly turn textual, public, and visual data into structured, addressable data at a machine scale. It is possible via the use of SDP. SDP is a data-solutions package that consists of services, capabilities, and solutions for expediting the process of turning unstructured data into structured data and actionable insights. It is accomplished via artificial intelligence (AI) and machine learning (ML). It is a platform that can be configured, and it features:

  • Connectors to many unstructured data sources
  • Paths for the prebuilt consumption of data
  • Workflow features that allow for the administration of entities, schemas, taxonomies, and user management functions
  • Services for the quality of data that ensure its completeness, traceability, and suitability
  • Providers of features to model management systems, including data application program interfaces (APIs), reports, visualizations, and integrators to such platforms.
  • Administrative ability to enroll new clients and manage existing ones
  • SDP can easily interact with other enterprise systems, enabling business process automation, improving coverage, quality, and turnaround time, and driving actionable insights from unstructured data both inside and outside of the organization.


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