January 1, 2024

Shilpa Ramaswamy

How Does Generative AI in IDP Solutions Help the Finance Sector?

Finance

Table of contents

Introduction

The finance industry heavily relies on data analysis to make informed decisions. While structured data such as credit, debit, and profit-and-loss documents drive the industry, there’s also a considerable layer of crucial but unstructured data that can present challenges in extracting valuable insights.

Organizations in the financial sector analyze a wide range of documents, from spreadsheets to emails, voice memos to images. They frequently face the issue of “dark data” – a euphemism for untapped resources that are hidden within unstructured documents. This type of information can hurt vital decision-making processes, risk reduction, user engagement, and compliance efforts if it remains unexplored. To unlock its potential, organizations must embrace innovative solutions.

What are IDP Solutions?

Traditional approaches like manual handling and legacy Optical Character Recognition (OCR) are not well-suited to tackle the nuances of unstructured data, leaving room for errors, inefficiencies, and knowledge gaps. This is where modern Intelligent Document Processing (IDP) solutions come in, powered by Unstructured Data Processing (UDP) platforms and featuring human-in-the-loop (HITL) functionality for model training and quality assurance.

IDP solutions excel in extracting, organizing, and processing complex documents or data types efficiently and securely, making them indispensable in numerous financial and administrative tasks, such as credit assessments, tax returns, loan agreements, and many others. By leveraging AI and ML techniques, IDP systems automate the process of extracting context and categorizing data, speeding up payment processing and reducing operational costs, while mitigating risks of theft and fraud.

With the emergence of cognitive automation for document processing, there’s a pressing need for enterprises to fully explore more complex opportunities using artificial intelligence methods.

9 Factors to Consider Before Implementing IDP Solutions

Internal vs. external documents

Organizations in the insurance industry often handle both internal documents like policies and contracts, as well as external documents such as claims and complaints. When it comes to internal documents, cognitive automation can be applied with ease. This is because such documents typically have a clear appearance and structured format, which can be leveraged using natural language processing to develop effective solutions.

For instance, insurance policies usually have standardized sections that follow a predictable order, with content for each section that differs only according to specific policy details. As a result, it’s easy to index and query these types of documents. However, when dealing with complaints from disgruntled customers, the structure can be difficult to predict — even more so when they’re scanned copies of poor visual quality. Incorporating automation into such processes is more challenging and raises issues of development feasibility.

Flawless recognition of handwritten content

Handwriting recognition has improved significantly in recent years through different solutions and cloud APIs. However, achieving near-perfect accuracies, like 90% or higher, is still challenging. The context in which the technology is used affects its performance. Recognizing numbers or letters in specific boxes on forms is easier than deciphering hastily scribbled words or phrases in complaint letters. Projects that rely on flawless automation of handwritten content recognition often face feasibility concerns.

Having multiple look-up systems

Typically, multiple additional systems are involved in data entry or document processing. It might be necessary to check a patient’s eligibility for a specific medical treatment or search for standard claim codes, for example. Timelines for these tasks typically increase as the number of systems to consult increases due to integration challenges and coordination with internal IT teams.

Access to historical documents

To create an effective AI-based cognitive automation solution, the development team must have access to a large sample of historical documents. These samples are often referred to as “dumps” and they serve as training material during the development process to ensure that the solution can handle various formats and variations. Unfortunately, obtaining these dumps from enterprise document repositories can be a challenge. These repositories may not have been designed with ease of extraction in mind, which means that collaborating with internal IT teams who own and manage them is often necessary to save time and resources.

Presence of near-real-time feeds

When implementing an intelligent document processing solution, it’s critical to have two kinds of near-real-time feeds: (a) a feed that provides the necessary documents as needed, and (b) a data feed that facilitates lookups for ancillary systems. Failure to secure these feeds quickly and efficiently is cause for concern from a Development Go/No Go standpoint.

Critical application integration

In the corporate world, there are processes involving extremely important documents, such as handling urgent complaints, that cannot be delayed. It is essential to develop an intelligent document processing solution that works consistently to provide production support in order to ensure reliability. Although this may increase costs and timelines, it is crucial to maintain a smooth workflow for these significant documents.

Opting for centralized or decentralized solutions

When considering the deployment of a potential solution, it’s important to weigh the benefits and drawbacks of centralization versus decentralization. A centralized solution can be easier to deploy and debug but comes with higher costs. On the other hand, a decentralized deployment may present maintenance challenges but offers a lower-cost alternative. Whether there is full automation or human intervention required, this decision should be made at the outset to ensure a smooth implementation process.

Conducting feasibility assessments

To arrive at a Go/No Go decision on development, it is necessary to conduct a feasibility assessment. When proposing a solution, it is important to consider several factors to ensure successful adoption and financial viability. The two key factors include:

a) Coverage, which identifies the necessary percentage of the area under consideration that must be adequately managed by the proposed solution.

b) Accuracy, which focuses on both individual data-entry fields and entire documents.

By keeping these factors in mind, we can confidently move forward with exciting new solutions that meet all requirements for success. If high levels of both coverage (above 66%) and accuracy (above 80%) are deemed essential for the project’s success, this should raise an early red flag where further investigation is needed.

Turnaround time

To conclude, the execution team needs to consider how responsive is the IDP solution. The specific “user experience” requirements may vary depending on whether it includes a chatbot, user interface, or integration with fast-paced applications.

Generative AI in IDP Solutions: The Morgan Stanley Case Study

What happens when you integrate your IDP software with Generative AI solutions? Generative AI-powered intelligent document processing offers numerous advantages to businesses. It enhances efficiency by eliminating manual efforts and saving time in document processing. With its exceptional ability to understand and extract information accurately, it ensures improved data accuracy and integrity. By leveraging generative AI-powered IDP, businesses can gain valuable data insights, uncovering patterns and trends for informed decision-making and a competitive edge. Additionally, this technology enables swift retrieval of specific information within documents, streamlining workflows and increasing productivity. It also enhances compliance by automatically identifying sensitive data and reducing operational costs associated with manual tasks, making it a cost-effective solution.

Morgan Stanley, a long-standing presence in the business world, has embraced generative AI-powered intelligent document processing. With a century’s worth of market research, investment strategies, and insights, the company faced the challenge of efficiently searching through vast amounts of information. By incorporating GPT-4 into their document processing, Morgan Stanley’s advisors now enjoy nearly instantaneous access, processing, and synthesis of content. Jeff McMillan, Head of Analytics, Data & Innovation for Morgan Stanley Wealth Management, aptly describes it as knowing the most intelligent person in Wealth Management available instantly.

How does Akaike’s BYOB Mitigate Common IDP Challenges?

Intelligent Document Processing (IDP) comes with its fair share of challenges when it comes to handling various document formats. From PDFs and images to scanned documents, there’s a need for multimodality to ensure accurate data extraction. That’s where BYOB steps in.

BYOB utilizes advanced Optical Character Recognition (OCR) technology and Natural Language Processing (NLP) algorithms to interpret and convert different formats into machine-readable text. This not only enhances contextual understanding but also improves accuracy in data extraction.

We understand the importance of maintaining high levels of accuracy in data extraction. To achieve this, our proposed solution involves implementing machine learning models that continuously learn and adapt based on feedback. This approach helps minimize errors and greatly improves decision-making capabilities.

At BYOB, we prioritize security and privacy concerns. We take a proactive approach by implementing robust encryption and access control to maintain an immutable record.

Scalability and integration are key aspects that we also address. Our solutions offer on-demand scalability options and seamless integration with existing business software, ensuring a smooth transition for your organization.

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