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Structured Data Is the Key to AI for Banks & Insurers

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October 6, 2021
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October 6, 2021
Structured Data Is the Key to AI for Banks & Insurers

AI – The most defining technology in the financial services and insurance industries

While financial institutions are still in the early stages of adopting AI technologies, intelligent machines are expected to become the most defining for the future of institutions in the financial services and insurance industries. Estimates suggest that 75% of insurance executives believe that AI will significantly alter or completely transform the insurance industry by 2020. Moreover, one-third believe that AI will entirely transform their own company within that timeframe.

AI is expected to redefine how financial institutions gain information from and interact with their customers, with the benefits of embedding AI into user interfaces being better data analysis and insight. A range of institutions is experimenting or implementing AI/ML capabilities into various processes, with benefits encompassing improvement of straight-through reconciliation (STR) of incoming payments, higher conversions from service recommendation engine, a better understanding of customer behavior and preferences, large-scale automation, etc.

Among the financial institutions, Goldman Sachs is one of the most vivid examples of institutions embracing the potential of AI. Goldman Sachs brings significant automation into trading areas like currencies and futures using complex trading algorithms, some with machine learning capabilities. For example, MIT Review reports that the number of US cash equities trading desk at Goldman Sachs’s New York headquarters employees went from 600 in 2000 to just two equity traders and automated trading programs supported by 200 computer engineers doing the rest of the work.

HDFC, ICICI, BofA, Charles Schwab, and JPMorgan are also among the institutions applying AI/ML across various use cases.

The insurance industry has its own examples. Allianz, MetLife, Transamerica, QBE Insurance Group, XL Catlin, and Aetna have explored various applications of AI, and some of them have reported important results.

MetLife, for example, has reported that Shift Technology, an AI startup based in France, helped a European coalition of insurers to analyze 13 million claims. As a result, the technology identified 3,000 new potential fraud cases, including a large, organized crime scheme that impacted nearly all the coalition’s members. According to a Shift Technology case study, the scam had siphoned millions of euros from the group’s insurance company members over many years.

There is no shortage of examples of how intelligent machines address critical areas of the financial services and insurance industries. Still, all of them rest on a single defining foundation – data.

Structured data is the foundation of successful AI adoption

While adoption of machine learning and artificial intelligence is critical for success in the era of rapid digital transformation, it’s even more critical how organizations structure data to make it usable for driving insights.

Data drive financial services and insurance industries. How data is leveraged has an incredible impact on the bottom line and customer satisfaction. However, despite expected benefits and the abundance of available technological advancements applicable to various elements of the value chain and operations in the financial services and insurance industries, institutions are yet to harness the potential of AI fully. The main reason for that is the complexities of organizing data that feeds intelligent machines.

As Jon Theuerkauf, former Managing Director & Group Head of Performance Excellence at BNY Mellon, said, “Forget AI. I don’t even know what it means. Why are we jumping on it if we haven’t done the basics? (referring to structured data being the key to AI.)We are now in a transitional phase and are still three to five years away from integrating an operating automated environment. For example, it takes a long time to train Watson. Why? Because the data does not land itself easily to allow Watson to learn. So, there needs to be an order around that data, and we are now starting to put things together and taking the chaos out of it.”

80% of modern data is unstructured, representing a security risk and inhibiting the adoption of advanced technologies

“Like the physical universe, the digital universe is large – by 2020, containing nearly as many digital bits as there are stars in the universe. It is doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes or 44 trillion gigabytes.” – IDC

However, the vast majority of data representing the digital universe is and will remain unstructured. And although unstructured documents are widely used as key inputs and systems for core business activities, there are significant challenges organizations face when it comes to unstructured data, which include managing and extracting value from the influx of unstructured data, processing these huge volumes of data as quickly as possible, and finding new and innovative technologies within their industry.

Moreover, unstructured data is seen as a vulnerability in the face of cyber threats. Because organizations struggle to understand where that critical unstructured data is, how it is used and who has access to it, it can represent a bigger risk to the enterprise, according to IBM. The level of risk varied significantly depending on the case. Still, one thing is common for unstructured data – the difficulty in applying a standard, organization-wide measure to protect that data and applying advanced technologies to drive value from the range of sources generating unstructured data.

Here is how 25% of global systemically important financial institutions (GSIFIs) in the US are maintaining their competitive advantage

The financial services and insurance industries are highly dependent on data-driven predictive analytics. Structured, organized data is critical for accurate and dynamic adjustment of financial products and services to continuously changing consumer habits and behaviors, as well as changing market conditions.

Today, structured data is the competitive advantage for 40% of the GSIFIs in the US (by AUM) who use a single solution – Pendo Machine Learning Platform (PMLP) by Pendo Systems. Pendo System’s machine learning platform transforms unstructured data into AI-ready datasets at a machine scale allowing businesses to explore, discover and analyze unstructured data accumulated across a wide variety of sources. Applying real-world customer training data, the Pendo Machine Learning Platform (PMLP) improves the accuracy of standard NLP libraries to over 95%.

Pendo has recently released version 4.0 of the Pendo Machine Learning Platform (PMLP) that incorporates several new capabilities.

The new release has a vastly improved toolset that significantly accelerates the time to implementation, as well as offers the ability to tackle more complex machine learning processing challenges. In addition, new features of version 4.0 allow to engage SMEs to create training data with the Pendo UI and then train models against it. This enables companies to put the solution in the hands of the business users, not just their IT groups.

Version 4.0 of Pendo Machine Learning Platform (PMLP) radically improves UI for managing complex classification and processing of documents. It also offers new connectivity options with Content Management Interoperability Services (CMIS) support and web crawling. Version 4.0 also brings new plugins that integrate seamlessly to provide access to a range of machine learning algorithms.

“The release of version 4.0 is a further demonstration of our ruthless commitment to continued product innovation. Empowering the business is an essential component of our strategy, and this approach is already paying real dividends in terms of new user adoption.

“Across the financial services and insurance spectrum, we are also seeing a number of common trends emerging in terms of the benefits our clients are already deriving from the Pendo Platform. Accelerating cost take-out strategies, automating lending decision support, improved policy control, enabling better AML, and enhanced STP & Risk Management capabilities, to name just a few, all feature very highly on their agendas, and there is so much more we can deliver.

“We truly believe the Pendo Platform is changing the way our clients can exploit the value of unstructured data. Version 4.0 is another major step on the journey and is already enabling Pendo to help unlock even more value to our users,” said Pamela Pecs Cytron, CEO of Pendo Systems, in the official announcement of Version 4.0 of the Pendo Machine Learning Platform (PMLP).

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