Technical FAQs

It doesn’t matter if you are a small startup or an enterprise giant, or if you have in-house development teams or contractors. The effectiveness of your tech teams is an integral part of your business success and strategic growth.

We live in a world driven by technology, and technology is changing fast. Companies can’t escape this reality. It’s either evolve with technology or become extinct. Don’t take my word for it. Think about the evolution of technology in industries like transportation (Uber), retail (Amazon), and video (Netflix). You can try to escape reality, but you will probably fail.

One of the first things that comes to mind when talking about software development teams is to ask if teams are absolutely necessary. Can’t we rely on individual tech professionals instead of teams working for our companies? Maybe the whole is not more than the sum of its parts?

The fact is that, in general, teams outperform individuals. When people work in a team toward a common goal, they combine their skills. In a team, individual performance increases, and people are able to solve more complex problems, efficiently and effectively.

My name is Joshua Candamo. I’m a technology leader with a PhD in computer science. My background is pretty diverse, and includes considerable experience programming as well as over 14 years of technology leadership.

I am currently a Director of Development for Accusoft, a software development company specializing in content processing, conversion, and automation solutions. My engineering group collaborates with about 40 people including in-house software developers, offshore contractors, technical writers, product management, quality, marketing, and sales professionals.

I want to share what I’ve learned from my personal experience of building development teams over the last 14 years, and a few useful tips to doing so successfully.

Without further ado, let’s talk about the three simple things that I found can make or break development teams.

To get started, let’s point out the obvious. Don’t fight nature; embrace it.

If you try to plant a rose in the middle of the desert, it will most certainly die.

You can’t fight nature. However, if you understand nature, you can embrace it and make decisions that align with it.

You can simply build a greenhouse in a harsh environment and succeed at growing a rose pretty much anywhere. Using the same logic, there are some foundational pieces that you have to anticipate in order to build a successful team. Avoiding basic considerations of team building will likely make your development team fail or underperform.

Team building is a broad and complex topic. And, it’s also a topic that I’m passionate about. Not everything around team building is complicated. However, most initiatives require a methodical approach to correctly execute them.

I’ll go over three ideas that are straightforward to implement, and don’t require major capital investment. Learn more in the rest of my article here.

Accusoft’s FormSuite for Structured Forms is a powerful SDK that allows you to integrate character recognition, form identification, document cleanup, and data capture capabilities into your software applications. You can set up unique form templates based on your processing needs and then design customized output architecture to extract data for delivery to a database or other downstream applications, helping you get to production faster or bring a new level of functionality to your legacy systems.

Setting all of that functionality up, however, can be a daunting task, especially if you’re working with a wide variety of form types. That’s why our FormSuite enablement services team is available to help you implement the features you need to ensure lasting results. Whether you’re facing bandwidth constraints or lack the resources to build expertise quickly, our FormSuite experts bridge the gap to make your project a success. Our enablement services team takes a five step approach to every engagement.

The Accusoft Approach to Enablement Services

Step 1: Thorough Architecture Review

We start by conducting a top to bottom analysis of your production or operational environment. Our review not only evaluates your system architecture and data workflow, but also breaks down the details of your potential use cases and existing work samples. 

Step 2: Identifying the Right Fit

Next, we determine the best FormSuite options based on your unique requirements and build you a custom enablement plan that will equip you with the instruction and assistance you need for a successful implementation.

Step 3: Training Your Team

Armed with information about your application’s specific requirements, we develop a customized training program to give your team a solid foundation for future development and ongoing maintenance. From guidance on form template creation and image enhancement to working with the forms API, we provide you with targeted guidance designed to help you solve potential challenges unique to your application environment.

Step 4: Implementation Support

Once the training is complete, you’ll have the foundational knowledge required to build the forms processing workflows your application requires. Our FormSuite experts remain on call to answer your questions so you can achieve your integration faster and ensure that you’re processing forms accurately.

Step 5: Preparing for Long-Term Success

Our enablement services prepare you to manage your implementation over the long term. We not only show you how to maintain the current environment, but also identify potential opportunities to deploy new features as your application scales in the future.

Keep the Partnership Going

Following your integration, we also provide ongoing support options to our customers whether or not they’ve utilized our enablement services. You get free Upgrade Support for 90 days after initial purchase, which includes email support and product upgrades. After that period, you can extend Upgrade Support, or elect to transition to our Standard Support or Priority Support annual plans.

To learn more about FormSuite for Structured Forms enablement services, talk to one of our solutions engineers. We’re ready to help you get your integration started!

The insurance market is booming. As noted by research firm Deloitte, the property and casualty (P&C) sector saw a massive income uptick in 2018 and steady growth last year that’s predicted to carry forward through 2020. To help manage the influx of new clients and handle more claims, many firms are spending on insurance technology (insurtech) — digital services and solutions that make it possible to reduce error rates and enhance operational efficiency. InsurTech SDKs are important components of this transformation.

Both in-house insurtech solutions and third-party platforms often excel in specific areas but come up short in others, putting insurance firms at risk of writing off potential gains. While solution switching and ground-floor rebuilds offer one route to success, there’s another option that’s more custom to your business needs: software development kits (SDKs). Here’s a look at three top SDKs that offer customized functionality potential.

FormSuite for Structured Forms: Solving for Data Capture

Time is money. The faster insurance companies accurately complete and file documents, the greater their revenue potential. And as noted by KPMG, the need for speed is more pressing than ever. Many insurance sectors have seen substantial increases in both claims and new applications as the COVID-19 crisis evolves. 

As a result, accurate and agile forms processing is critical to keep up with demand. If current insurance software can’t quickly capture forms data, recognize standard form fields, and let users easily create standard form libraries, policy processing falls behind.

FormSuite for Structured Forms makes it easy for developers to build in form identification and data capture that includes comprehensive form field detection with OCR, ICR, and OMR functionality and the ability to automatically identify scanned forms and match them to existing templates.

ImageGear for .NET and C/C++: Simplifying Conversion

Conversion is critical for insurance firms. Depending on the type and complexity of insurance claims, companies are often dealing with everything from Word documents for initial client assessments and .GIF or .JPG images of existing damage to contractor-specific PDFs or spreadsheets that detail necessary materials, time, and labor costs. The result? A mash-up of multiple file types that forces adjusters to spend valuable time searching for specific data instead of helping clients get their claims process up and running. This makes it difficult to recognize value from emerging digital initiatives. 

Accusoft’s ImageGear for .NET and ImageGear for C/C++ empower developers to integrate enterprise-class file viewing, annotation, conversion, and image processing functions into existing applications, allowing staff to both quickly collaborate on key tasks and find essential data across a single, easy-to-search document.

ImageGear: Streamlining PDF Capabilities

While insurance technology offers substantive opportunities for end-users to capture, convert, and retain data, this technology can also come with the challenge of increased complexity. According to recent research from PWC, for example, firms looking to capitalize on insurtech potential must be prepared to rapidly develop new product offerings and embrace the expectations. 

As a result, companies need applications that streamline current functions and allow them to focus on creating cutting-edge solutions. For example, PDF is a file format that is still used by enterprises worldwide to maintain document format consistency and maximize security. When it comes to converting multiple files into a PDF, software can be expensive and introduce data security issues. 

This can all be solved with an SDK like ImageGear, which makes it possible to integrate the total PDF package into any document management application, both reducing overall complexity and freeing up time for staff to work on new insurance initiatives.

Insurtech forms the framework of functional futures in policy applications, claims processing, and compliance reporting, but existing software systems may not provide the complete capability set companies need to make the most of digital deployments. These top SDKs offer insurance IT teams the ability to integrate key services, improve speed, and boost security at scale. Learn more about Accusoft’s SDKs at www.accusoft.com/products. 

Barcodes continue to be an essential tool for today’s organizations, whether they’re using them for managing supply chains or sorting documents within a complex digital workflow. Since the early 1990s, however, the potential use cases of barcodes have expanded tremendously. That’s largely due to the invention of the quick response barcode, better known as the QR Code. Developed by the Japanese manufacturer Denso Wave in 1994, this two-dimensional barcode revolutionized the way data was encoded and scanned. Today, QR Codes can be found practically everywhere, along with their smaller cousins, the Micro QR Code.

What Is a Micro QR Code?

Although the standard QR Code could hold a tremendous amount of information, that ability occasionally created challenges for specialized use cases where space was at a premium. Small components like circuit boards or machinery parts, for example, often couldn’t accommodate a QR Code. Even when they could, much of the QR Code’s storage capacity wasn’t being used to its full potential. For use cases where space was at a premium and only a small amount of data needed to be encoded, a more compact version of the QR Code was needed.

The Micro QR Code was designed to solve this specific challenge. Roughly half the size of the conventional QR Code, this smaller version still provided many of the benefits of its bigger cousin, including finder patterns to orient the image properly, multiple levels of error correction, and support for Japanese Kanji, Kana, and Hiragana characters.

The Anatomy of a Micro QR Code

A Micro QR Code consists of four elements that allow it to encode data and provide a barcode reader with instructions for how to read the contents.

Data Modules

Like any other QR Code, Micro QR Codes store binary data in square modules. While the human eye only registers the black modules, a computer scanner also registers white modules when reading the code. A black square represents a binary 1 while white squares are read as a binary 0. The amount of information that can be encoded into these modules changes depending upon the size of the barcode. Micro QR Codes can be written in four different sizes (more on that in a moment), allowing them to store up to 35 numeric digits, 21 alphanumeric characters, or 128 data bits.

Finder Pattern

The finder pattern is the square “bull’s eye” that appears in the upper-left hand corner of a Micro QR Code. This pattern ensures that the barcode is oriented and scanned correctly when read by an application. Since Micro QR Codes contain less complex data, they only require a single pattern finder while a conventional QR Code uses three. While many QR Codes also require an alignment pattern to correct for crookedness or distortion, Micro QR Codes are not large enough for these problems to create much of an issue during scanning.

Timing Pattern

A series of alternating black and white modules running vertically along the left side and horizontally along top of the barcode, the timing pattern is used to configure the rest of the data grid for the scanner. By reading the timing pattern, the scanner software can quickly determine the size of the barcode’s data matrix, as well as the symbol and version density.

Quiet Zone

A clear margin space surrounding the rest of the barcode elements, the quiet zone makes the boundaries easy for scanning software to detect and identify. While a conventional QR Code requires four or more modules of empty space, a Micro QR Code only needs a two module-wide space. This helps to keep the barcode compact regardless of how much data is encoded within it.

Micro QR Code Sizes and Error Correction

Depending upon the amount of data encoded, Micro QR Codes can be written in one of four sizes. The smallest version, M1, consists of 11×11 modules, while the largest, M4, is 17×17 modules. Each size above M1 can support different levels of error correction, although the more thorough the error correction, the less data can be encoded.

Error correction is based on the Reed-Solomon algorithm and allows scanning software to recover lost, poorly printed, or damaged barcode data. Versions M2 and M3 offer two levels of error correction:

• Level L (Low): Capable of recovering up to seven percent of encoded data.
• Level M (Medium): Capable of recovering up to 15 percent of encoded data.

As mentioned above, higher levels of error correction impact the amount of data that can be encoded into Micro QR Code modules. That’s because the redundancies necessary to support error correction algorithms take up available space. Increasing an M3 barcode’s error correction from level L to Level M, for instance, would reduce the number of numeric characters that could be supported from 23 to 18.

An M4 Micro QR Code contains enough modules to support a third level of error correction:

• Level Q (Quartile): Capable of recovering up to 25 percent of encoded data.

Although level Q provides excellent durability, it leaves much less space for encoding data. An M4 barcode with this level of error correction actually holds less data than an M3 barcode with level L error correction. When writing a Micro QR Code, it’s important to determine what level of error correction is actually necessary for the use case at hand rather than simply defaulting to the most robust option.

Differences Between Micro QR Codes and Conventional QR Codes

While Micro QR Codes use many of the same 2d barcode principles as traditional QR Codes, it’s not quite accurate to think of them as a condensed version. They have some notable differences that make them more or less suited to specific use cases.

Micro QR Codes

• Provide up to three levels of error correction.
• Needs only a single finder pattern for orientation.
• Can encode up to 128 bits.

Conventional QR Codes

• Provide up to four levels of error correction.
• Requires three finder patterns for orientation.
• Can encode up to 23,658 bits.

Enhance Your Barcode Capabilities with Barcode Xpress

Adding barcode recognition capabilities to an application can help to streamline document management workflows and allow organizations to route files more efficiently. Developers can easily integrate the ability to read and write barcodes into their platforms using a barcode SDK like Accusoft’s Barcode Xpress. With support for more than 30 unique barcode types, including Micro QR Barcodes, this versatile SDK provides the tools to support a wide range of use cases that call for fast, accurate barcode recognition.

For a hands-on evaluation of how Barcode Xpress will perform in your development environment, download a free trial today or start a conversation with one of our SDK specialists.

Seventy-six percent of companies surveyed plan to prioritize machine learning (ML) and artificial intelligence (AI) deployments in 2021. Despite increased uptake, however, there is still a great deal of confusion surrounding these advanced concepts. In order to understand how organizations hope to leverage ML and AI in their technology initiatives, it’s helpful to take a step back and examine how they work and how they differ from each other.

What Is Machine Learning?

Machine learning uses statistics-driven algorithms to find patterns in massive amounts of data. These algorithms are designed to improve over time as they process more data to enable more accurate outputs. Machine learning is widely used to produce predictive recommendations — companies such as Google, Netflix, and Facebook collect data about user behaviors and feed it into machine learning algorithms which then produce targeted search results, movie recommendations, or advertisements. 

The key to machine learning success is data. The more data available to ML algorithms — and the higher-quality this data — the better they’ll be able to identify patterns in current datasets and apply them to new data sources.

Most machine learning methodologies fall under one of two broad categories:

• Supervised Learning: Developers classify and label data to guide the algorithm’s inputs and outputs to ensure specific patterns are recognized. This method is time and resource intensive because it requires data scientists to capture, control, and curate data sources.
• Unsupervised Learning: This approach provides ML algorithms with unlabeled and unclassified data and allows them to identify patterns based on unique data characteristics. Developers don’t interfere with the learning and pattern recognition process, instead evaluating the outputs for accuracy and modifying code as needed.

Why Does Machine Learning Matter?

Machine learning helps organizations leverage the massive amounts of data they’ve accumulated. This information is drawn from a variety of sources, including disparate forms and documents, data produced through customer transactions and service calls, and the ongoing operational data produced by staff as they interact with IT resources.

Thanks to both the rapid uptake of cloud computing and availability of large-scale data collection and analysis tools, these data volumes are increasing exponentially. As a result, aggregate assessment is now critical — companies need a way to rapidly and reliably derive patterns from available data, and apply these patterns to predictive action.

This is the evolving role of machine learning. By creating, testing, and deploying ML algorithms capable of rapid pattern analysis and application it’s possible for companies to benefit from this continual data influx rather than being constrained by the bounds of traditional data evaluation. To facilitate this process, many next-generation software tools and services are either equipped with built-in ML frameworks or are capable of interfacing with them.

Key Machine Learning Applications

The applications of machine learning are vast, but they tend to produce the best results when paired with existing processes that supplement human efforts or automate low-value, but labor-intensive, functions in the workplace. In effect, it has the potential to do almost anything a human mind can do, given enough time. 

Improved Data Capture

Capturing data from internal documents and customer-submitted forms can be cumbersome and time-consuming. It can also lead to wasted time and effort if data is incorrectly entered, duplicated, or accidentally deleted. By pairing machine learning tools with forms processing solutions like Accusoft’s FormSuite for Structured Forms, developers can build applications that identify, collect, and capture key data more efficiently and accurately. For example, a robot process automation (RPA) bot can be set up to receive extracted form data from FormSuite and then populate that information into the appropriate fields within an application. This not only accelerates forms processing workflows, but also greatly reduces the risk of data entry error. Properly implemented, automated data capture can act as a springboard for improved data insight and decision-making thanks to improved accuracy and consistency. 

Streamlined Content Creation

By combining machine learning algorithms and data sources with document editing tools, it’s possible to streamline key processes such as the creation of complex, compliance-bound content. One in-practice example is the use of Accusoft’s PrizmDoc Editor within the LegalSifter contract review and creation platform. By pairing its AI technology with PrizmDoc Editor’s document assembly capabilities, LegalSifter was able to quickly locate repetitive clauses and suggest replacements to create an automated contract creation experience for end users. 

What Is AI, and How Does It Relate to Machine Learning?

The terms artificial intelligence and machine learning are closely related and often used interchangeably, but they’re not identical.

Artificial intelligence refers to technologies that are capable of performing tasks like photo recognition or data pattern analysis with similar (or better) outcomes than human beings. Machine learning refers to the creation, testing, and refinement of the algorithms needed to support AI tools. In many ways, then, ML functions as a distinct process that helps make AI possible.

As noted by Toward Data Science, it often helps to think of AI, machine learning, and deep learning like a set of concentric rings. The smallest, inner ring is deep learning, which helps inform the middle ring of machine learning by providing layered neural network structures that improve the process of pattern recognition. The final, outside ring is AI, which depends on both deep and machine learning to deliver real-world results. 

Artificial intelligence tools can be broken down into two basic types:

• Generalized AI: These tools are capable of solving problems bounded by a clear set of rules. Using the ML algorithms that underpin the larger AI structure, general AI applications can act on stimuli — such as a security alert from an IT network — and respond appropriately by creating and logging reports or looping in human agents. 
• Narrow AI: These solutions are designed to solve specific, small-scale tasks. Building on the security example from above, a narrow AI application might see tools responding to specific threat events such as DDoS or ransomware attacks by deploying targeted, defensive responses that close active sessions, capture attack data, and prevent future connections from the same IP address. 

In practice, narrow AI tools can outperform their human counterparts in completing specific tasks, but are unable to translate this expertise into applicable action at scale. General tools come closer to mimicking human intelligence but are still a long way from replicating the depth and breadth of human thinking.

Limitations of AI

Much has been made about the potential of AI technologies to take the place of human staff, leading to a generalized sense of worry about the future of these tools at scale. Recent research, however, found that substantial confusion remains around not only the deployment of AI but the definition itself. In fact, one study found that 40 percent of AI startups in Europe were not actually using AI. In some cases, increasing market interest in AI tools encouraged the use of this term to help startups capture attention, in much the same way that rapid cloud adoption spurred the creation of a host of “cloud” companies that offered nothing of the sort.

Uncertainty around AI itself, however, also plays a role in this disconnect. Given the massive potential of AI to help companies solve both specific and generalized problems, the term can be applied in almost any context and made to fit almost any description.

Unlocking the Future

After spending many years confined to research projects and future-focused technology articles, both machine learning and artificial intelligence are making their way into the applications and software companies are deploying every day. As developers look ahead to building the next generation of technology solutions, they must not only think about how they can better leverage ML and AI principles, but also how to implement features that take advantage of them.

Accusoft’s collection of versatile SDK and API integrations deliver powerful viewing and image processing capabilities that help applications streamline workflows and enhance productivity. To learn more about how Accusoft can help you enhance the workflow in your machine learning or artificial intelligence projects, contact us today.

Although it might feel as though time has been standing still for several months, 2020 is finally coming to an end. It’s been a year of unprecedented disruption for many industries, and insurance companies often found themselves struggling to adapt to change. Firms that had the foresight to invest in digital transformation backed by InsurTech solutions, however, proved more capable of meeting the moment and are now poised to thrive in 2021 and beyond.

As the new year approaches, it’s helpful to take a look back at some of the key trends that defined 2020 and created opportunities for innovative InsurTech applications. Understanding the pressures facing the insurance industry will also identify InsurTech projections to watch in the future.

5 Insurance Trends and InsurTech Projections

1. Remote Collaboration

No discussion of 2020 insurance industry trends would be complete without exploring how the COVID-19 pandemic has affected organizations. According to a survey conducted by Deloitte, 48 percent of insurance executives agreed that the pandemic revealed how unprepared their business was for such a disruptive crisis. From the sudden transition to a remote workforce to a shift in risk adjustment factors across the market, insurers have had to scramble to adapt their operations and continue delivering quality services to clients.

With so many employees going remote and customers unable to meet with representatives in-person, organizations that made early investments in digital collaboration tools and automation software were better equipped to meet the challenges of 2020. The industry is expected to make tremendous investments in digital transformation in the upcoming year, whether it’s in powerful document editing and management software, file conversion tools, or secure communication channels that better facilitate true collaboration.

2. Customer Demographic Shifts

Prior to 2020, insurance customers tended to be older, with millennials purchasing life insurance policies at lower rates and often delaying home ownership until later in life. This trend seems to have reversed itself in the wake of the COVID-19 pandemic, however, with life insurance application activity growing twice as fast for people under 45 than those aged 45-49. Since many of these younger buyers are first-time applicants, it’s important for insurance agents and firms to make the process as streamlined and easy to navigate as possible.

According to a nationwide industry survey, just over 75 percent of independent insurance agents are age 50 or older and nearly 68 percent have more than 20 years of experience. Having such a long-tenured workforce poses challenges when it comes to implementing new processes and reaching out to potential customers with different needs and preferences than those of earlier decades. In order to remain competitive, however, firms must invest in the right InsurTech solutions to gather data that will give them a better picture of what insurance products and services younger customers will find attractive.

3. Robotic Process Automation

The shift to a remote workplace greatly disrupted traditional workflows. Without centralized offices, key insurance tasks like claims processing and document verification are much more difficult to perform manually. Organizations that had already invested in robotic process automation (RPA) to handle repetitive tasks were in a much better position to thrive in a remote landscape. 

Insurance companies must be able to process a variety of forms during an application or a claim. Having automated InsurTech tools in place to quickly extract data from a variety of sources and carry information over from one form to another not only saves time, but also greatly reduces the risk of human error. For a remote workforce, automation software helps to consolidate complex workflows to eliminate version confusion and enhance collaboration.

4. Artificial Intelligence

Risk assessment and data analysis are crucial to the underwriting process. In a volatile economic environment, insurance firms are under more pressure than ever before to set the right premiums. Although the data is now readily available to make more accurate assessments, sorting through that information manually is difficult and time consuming. 

As the COVID-19 pandemic demonstrated, the scope of risk can change dramatically in a very short period of time. By deploying artificial intelligence (AI) to analyze risk factors and review potential fraud claims quickly and accurately, insurers can create customized policies and provide more responsive service to their customers.

Implementing AI-driven algorithms as part of the underwriting and fraud analysis process will only be one part of the challenge facing firms in 2021. These powerful tools must have sufficient data in order to make informed predictions. By improving the data collection process with form processing tools, file conversion, and programmatic searches, insurers can provide their analytics platforms the best possible information for analysis.

5. Customer Experience

One of the few positive impacts of the COVID-19 pandemic was that it forced organizations across every industry to find new ways of connecting with their customers. The insurance industry has traditionally lagged behind other sectors, tending to lean upon a combination of tradition and legacy infrastructure to engage with customers. But the events of 2020 have underscored the need for a true digital transformation that fundamentally reorients the way firms market, sell, and deliver insurance products. The need has become so evident, in fact, that a recent PWC survey found that 70 percent of insurance CEOs are prioritizing customer experience and user interfaces as their top investment opportunity.

In the coming year, firms will likely continue to invest in technology that makes it easier for customers to research and manage their policies. Whether it’s applications that allow them to submit claims information in a variety of file formats or forms and contracts that automatically fill in commonly used form fields, the core focus will be on making the customer experience as frictionless as possible with a variety of InsurTech benefits.

The Role of InsurTech

Many insurance companies will be looking to upgrade their technology stack and client-facing applications in response to these trends. That creates a tremendous opportunity for InsurTech developers who are creating the next generation of software tools to streamline core processes common to the insurance industry.

Delivering those digital products on a short timeline with limited resources, however, can be quite a challenge for even the most innovative InsurTech startup. That’s why many of them turn to third-party solutions to provide proven functionality that lies outside the scope of their development expertise. 

Features like forms processing, document conversion, and image viewing can be easily integrated into an application using an SDK or API, saving the team weeks or even months of work. This helps InsurTech companies get their products to market faster to meet the digital transformation needs of their customers and keep them a step ahead of their competitors.

InsurTech SDKs and APIs

Accusoft’s family of processing and automation SDKs and APIs provide InsurTech developers with the tools they need to easily plug essential functionality into their applications so they can get back to focusing on their most innovative features. With a variety of deployment options and a diverse set of code-based solutions, we have the flexibility to meet your software’s unique use case and substantially reduce your time to market.

Whether you’re looking to integrate document viewing, collaboration, or processing to your InsurTech platform, our SDK and API-based products can help you deliver the InsurTech benefits your customers are looking for. Learn more about our insurance solutions or contact us today to demo one of our products.

After years of discussion and debate over the state of digital transformation in the legal field, 2020 delivered something of an ultimatum to an industry that has proven historically resistant to drastic change. The COVID-19 pandemic profoundly altered the way many law firms do business, forcing them to seek out a variety of LegalTech solutions to survive in a new environment. Many of these changes are likely to remain firmly entrenched in the coming years, so it’s worth taking a look back at the factors driving them.

COVID-19 and Change in the Legal Industry

From an outsider’s perspective, the legal industry might have appeared to be uniquely well-suited to adapt to the pandemic. Lawyers are high-skill workers with an extensive range of technology solutions at their fingertips to facilitate remote work. It’s easy to imagine a scenario in which many aspects of the legal process, from client intake to discovery to filing documents with the court, are handled virtually, without anyone needing to step foot outside their home office. 

The reality, unfortunately, isn’t so simple. While it’s true that there are several innovative tools available that could support remote work, the legal industry has long struggled to adopt them at scale. Part of that has to do with the culture of law firms themselves, which tend to be driven by a traditional business model that hasn’t changed much since the 20th century. 

Although the legal industry has benefited from technology throughout its history, the use of that technology has typically fallen not to the lawyers themselves, but to their support staff. From printing out reams and reams of documents to manually tracking time in minute-based increments, many lawyers cling to outdated and inefficient practices out of habit and aversion to change.

Although the Great Recession caused some disruption to the legal industry, the impact was not significant or lasting enough to make firms fundamentally rethink their billing and technology usage. That has changed in 2020. As the industry struggles to adapt to the realities of the pandemic, firms have been forced to engage in what Jennifer Leonard, Chief Innovation Officer for University of Pennsylvania’s Carey Law School, describes as “forced experimentation.” This includes implementing technologies already quite common in other industries, such as video conferencing tools and cloud-based collaboration software, as well as taking a more customer-centric approach to delivering legal services.

Key LegalTech Trends in 2020

The rapid transition to the remote workplace has forced legal firms to implement several years’ worth of technological change into the span of a few short months. Here are a few key LegalTech trends and needs that defined the industry in 2020.

Secure Online Communication

Successful transition to a remote work environment requires the right software tools to facilitate secure communication and collaboration. Lawyers not only need to be able to stay in direct contact with clients and colleagues, but also with the court system itself. With many judicial offices shuttered during the early months of the pandemic, courts have greatly expanded their use of e-filing, e-service, and online dispute resolution software. Various video conferencing platforms have also made it possible to conduct court hearings remotely. In a historic move, even the US Supreme Court chose to hear arguments over telephone.

With so many lawyers working remotely, however, security has become more important than ever. That’s because home networks and personal devices can present a variety of security risks. Sharing documents over unencrypted email rather than through more secure LegalTech applications could potentially compromise secure client information or legal strategies. That has driven firms to implement digital solutions that they might have been hesitant to adopt as recently as a year ago.

Online Legal Research

The research and discovery process has gradually been moving online for quite some time. According to research by the American Bar Association (ABA), nearly 70% of lawyers begin their legal research with a general search engine or paid online resource. All of that online research means that lawyers need to be able to securely access and convert multiple different file types. While many legal documents can be found in various online databases, they often exist in poorly scanned formats that are difficult to read or otherwise manipulate. In order to manage these documents effectively, firms need LegalTech applications with imaging and conversion tools that can perform image cleanup and then convert files into formats that are easier to work with.

Virtual Document Review

Whether they’re negotiating contracts or reviewing information as part of discovery, lawyers need to be able to annotate and redact documents without creating confusion over which edits are the most up-to-date. Version control has long been a challenge for the industry, whether it was multiple people working from different printed copies of a document or everyone having their own copy downloaded to a separate device. It’s no surprise, then, that LegalTech startups specializing in contract review software have had no difficulty finding investors during the pandemic. To meet the growing needs of remote legal firms, these platforms will need to deliver powerful editing and access control features that allow users to collaborate more efficiently.

Innovative Billing Strategies

Although law firms have historically weathered economic downturns better than the rest of the economy, the unique nature of the COVID-19 pandemic hit the industry hard in the first half of 2020. According to data gathered by Clio, billing and case volumes plunged in March and April before starting a slow recovery in May. That recovery has been uneven, however, punctuated by a few sharp declines even as overall caseloads return to baseline levels. Firms frequently responded by laying off staff, with 20% of firms having done so or expecting to as recently as July.

The pandemic has forced many firms to implement timekeeping and billing software to help improve efficiency and deliver more value-based services to their clients. Traditional billable hour approaches tended to discourage efficiency, so shifting to a more flexible and transparent system driven by digital tools can help provide firms with the flexibility they need to meet client needs under adverse conditions. Automating billing also allows legal teams to focus more on acquiring new clients and retaining existing clients.

More Changes Coming in 2021

Several legal industry trends from 2020 are expected to continue, or even accelerate, in 2021. Here are just a few areas that will likely remain key priorities for LegalTech developers seeking to meet the industry’s needs.

• Improving the Client Experience: With so much of the attorney-client relationship going remote, legal firms will need to continue investing in tools that allow them to communicate and interact with their customers more easily.
• More Cloud Adoption: Legal firms have been slow to adopt cloud-based LegalTech applications, but the pandemic has demonstrated the value of being able to access essential data and tools from anywhere at any time.
• Organizational Innovation: As LegalTech becomes more essential, law firms will likely continue to rethink their organizational structure by adding non-legal staff to drive digital transformation.

Unlock Your LegalTech Potential with Accusoft

Developing robust LegalTech platforms that help firms overcome the challenges of the remote workplace is a major challenge. Accusoft’s collection of content processing and conversion solutions allow development teams to easily integrate the collaboration and information-sharing tools lawyers require into your applications. Whether you’re incorporating our REST APIs or powerful SDKs, we provide the functionality your software needs so your team can focus on the innovative features that will set you apart in the crowded LegalTech market in 2021 and beyond.

To learn more about how our content solutions can enhance your legal applications, talk to one of our integration experts today.