The financial services industry is in the midst of a massive transformation. Financial technology companies are at the forefront of this change; they are introducing innovations such as hyper-personalization leveraging capabilities in Artificial Intelligence (AI), conversational interfaces, blockchain, and crowdsourcing.¹ Thanks to these disruptions, the financial services industry is embracing innovation. While innovation is one side of the coin, compliance and regulations are the other. Financial institutions therefore are investing significantly to augment their data capabilities to make sense of transactional and enterprise data and comply with regulations. Some incumbent financial institutions, such as banks, spend more than 10 percent of their annual revenue on technology investments.² Here is the paradox: while banks continue to innovate and manage regulations through data-driven approaches, the truth is they find generating insights from their data to drive the transformation a challenge. Their analysis of data is, at best, inconsistent. Banks analyze structured data such as customer, credit, campaign, and product data. Out of 80 percent of the unstructured data available to banks, however, only 3 percent has been evaluated.³ Unstructured data—which include audio (customer interactions), video images (branch interactions), PDF documents (onboarding forms and regulatory documents), email files (communications), and, buried in the applications and Know Your Customer (KYC) forms, pay slips, mortgages, and other mission-critical documents—remain underutilized and unanalyzed.
Unstructured data offer financial institutions hitherto undiscovered actionable insights—of customer preferences, unmet customer needs, and market and process gaps. Banks, for instance, can use these insights to amplify customer experiences, enable new products and services, and conceptualize improved operating models. Interpreting unstructured data is too challenging, however; it is predominantly text heavy and often lacks standardization. It presents in many formats and multiple sources. Traditional data analysis tools do not work; unstructured data are not machine readable, which is vital for analysis using AI and machine learning.
Digital disruption in the financial services industry, combined with its highly regulated nature, has led to an exponential increase in the volume of unstructured data. Data from sources like passports, pay stubs, application forms, leases, loans, and mortgage portfolios must be combined with the available enterprise data to drive personalized customer experiences. Together, these data enable banks, asset management companies, and hedge funds to differentiate themselves and manage regulatory compliance while ensuring efficient operations. Straive’s text and public data intelligence solutions, enabled by its proprietary Straive Data Platform (SDP), help financial enterprises manage these unstructured data operations. Examples of Straive’s key solutions for the financial services industry include:
Automating document digitization and processing as part of mortgage and loan origination, customer onboarding, and KYC processes
Creating structured data sets that facilitate the reconciliation between the Mortgage System of Record and the System of Origination
Enhancing existing KYC processes by augmenting data from unstructured, internal, and external sources such as call records, court records, financial filings, news, and social media
Consolidating multiple watch lists for anti–money laundering monitoring and reducing transaction-monitoring false positives
Building alternate data sets from public records such as job openings, management changes, social media sentiment, product reviews, patent analysis, and court filings to generate alpha by identifying performance signals
Ensuring sales practice compliance with regulations and bank policies by monitoring customer call records, transcripts, and complaints
Collecting ESG reference data and building benchmark scores from unstructured sources such as annual reports, sustainability reports, and corporate social responsibility reports
Identifying and tracking ESG-related controversies and market-moving events from news wires and press releases
Enhancing the KYC process during underwriting by automating document classification and extraction to optimize customer onboarding
Extracting entities and critical metrics from forms and external sources for underwriters to evaluate risks
Extracting customer sentiments from internal and external communications to create enhanced customer 360-degree views
Assisting damage identification and payout estimation for Property and Casualty (P&C) claims settlements
Enabling searches for contracts, emails, forms, and other interactions for future audits and compliance purposes
The key to unraveling meaningful insights is SDP—the unstructured data engine. SDP is an AI-based platform with customizable extraction, enrichment, transformation, and delivery modules. It ingests unstructured data from diverse sources and transforms it into analytics-ready, integrable data to help enterprises gain actionable insights. How does SDP help financial institutions?
Financial institutions such as banks, investment companies, and mortgage firms can leverage SDP to rapidly transform text, public, and visual data into structured, addressable data at a machine scale. SDP is a data-solutions suite consisting of services, capabilities, and solutions for accelerating the process of converting unstructured data into structured data and actionable insights. It is a configurable platform with:
Connectors to multiple unstructured data sources
Prebuilt data-ingestion paths
Workflow capabilities to manage entities, schema, taxonomies, and user-management functions
Data quality services for completeness, traceability, and auditing
Delivery capabilities such as data application program interfaces (APIs), reports, visualizations, and integrators to model management platforms
Administrative capabilities to onboard and manage client projects
SDP integrates easily with other enterprise systems to enable business process automation, improve coverage, quality, and turnaround time, and drive actionable insights from unstructured data both inside and outside the enterprise.
Real-time screening and scraping of public data sources
Periodic monitoring and tracking of public data sources for data updates
Data cleansing, de-duplication, and normalization
API-based data integration with downstream enterprise system
Visualization tools for trends and patterns
Today, as financial institutions produce and consume more data than ever before, SDP offers businesses innovative methods of leveraging unstructured data and resetting operational and customer service benchmarks. SDP has processed and extracted data from more than 250 million unstructured documents.
Acquiring, enriching, and managing unstructured data is challenging. With the emergence of solutions such as Straive's text, public, and visual data intelligence solutions and platforms like SDP, deriving insights from unstructured data becomes simple. We expect strong momentum using unstructured data and platforms such as SDP for more diverse cases.
 Marr, B. (2019, December 30). The top 5 fintech trends everyone should be watching in 2020. Forbes. Retrieved October 26, 2021, from https://www.forbes.com/sites/bernardmarr/2020/12/30/the-top-5-fintech-trends-everyone-should-be-watching-in-2020/?sh=483aa3de4846.
 Bloomberg. (n.d.). Bloomberg.com. Retrieved October 26, 2021, from https://www.bloomberg.com/opinion/articles/2020-01-23/the-banking-industry-is-spending-wildly-on-the-latest-tech.
 2020, 20th O. (2020, October 19). Unlocking the benefits of unstructured data in banking. FinTech Futures. Retrieved October 26, 2021, from https://www.fintechfutures.com/2020/10/unlocking-the-benefits-of-unstructured-data-in-banking/
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