Businesses handle an immense amount of content daily, with most mission-critical processes being traced back to ingested content. Whether that be processing purchase orders to ship products on time or accurately interpreting refund requests to guarantee a positive customer experience.

While technologies like Optical Character Recognition (OCR) and Intelligent Document Processing (IDP) have helped accelerate content processing, the reality of non-standard and unstructured content has historically prevented enterprises from fully automating their processes from end-to-end. Yet today, with Applied GenAI Process Automation tools this is no longer the case. Many of IDP and OCR’s technical limitations have been bridged with the capabilities enabled through the strategic application of GenAI models.

In this article, we will clearly define the capabilities and limitations of OCR and IDP, and outline the key technical differences between these technologies and new GenAI-powered content processing solutions like our own Fisent BizAI.

Available Content Processing Technology: Definition, Capabilities and Limitations

Optical Character Recognition (OCR):

OCR is one of the most fundamental document processing technologies, in fact, it lies at the heart of almost every modern content processing pipeline. This technology transforms scanned copies or images of text and handwriting into machine readable text, that can then be manipulated or extracted for use within a software solution. 

Business workflow with structured documents, where content appears in a fixed format with data points in predefined locations can be processed by OCR systems. Using a defined set of rules and document coordinates, these OCR systems will extract data from specified fields of a given document. Think, pulling the TINs from tax forms or DOBs from passports.  

OCR limitations:

Intelligent Document Processing:

IDP builds on the capabilities of OCR enabling enterprises to automate the processing of semi-structured documents, where the content format varies but still follows a rough pattern (ie; Invoices or Purchase Orders). IDP does this by applying probabilistic machine learning models that learn from the data they process and by applying natural language processing (NLP) techniques to ‘intelligently’ gather data from documents with slightly varying formats.

IDP solutions train on processed content, with accuracy gradually improving through learning from human corrections (“reinforcement”). This means IDP solutions require human intervention, specifically for documents that vary from their training set. This method of progressive improvement means that IDP solutions are template based and typically require separate models for various document types within a business process. For example, a company looking to automate its purchase order processing would require separate IDP models to process invoices, purchase orders, receipts, and shipping notes.

IDP Limitations:

Applied GenAI Process Automation:

Finally, Applied GenAI Process Automation tools provide an option for processing content of all types, excelling at handling unstructured content. Unstructured content follows no format guidelines, is highly varied and possibly even multimodal. Some examples of unstructured content include contracts, emails with attachments or even complex legal documents.

Similar to IDP, these GenAI tools apply OCR to digitize text. However, instead of layering NLP or vertically trained ML models to identify key data fields and entities, they strategically apply LLMs to gather context, derive semantic meaning, and intelligently extract data from content, regardless of format or language. Furthermore, with multi-modal support for text, audio, video and images these models go beyond the scope of OCR and IDP by allowing content analysis beyond just text based documents.

These capabilities allow for some unique use cases typically out of scope for an IDP or OCR solution. Some examples include:

Business Impacts of GenAI-Powered Content Processing:

Increased Accuracy and Efficiency

With strong contextual understanding, GenAI Process Automation tools excel at accurately extracting data. At Fisent, our BizAI solution is leading the industry in Applied GenAI Process Automation.  In most of our use cases we’ve observed accuracy rates exceeding 93%, even with complex, multi-modal documents. Furthermore, by bridging the automation gap and enabling automated content processing of unstructured documents, organizations can fully automate key business processes from end-to-end. This means drastically reduced processing time and more time made available for employees to complete higher value tasks. For example, PC Connection recently reduced their Purchase Order processing time by 98% as a result of integrating BizAI into their workflow. 

Scalability, Security and Adaptability

Progressing from IDP to GenAI powered solutions doesn’t just provide users with the ability to process unstructured content, but also means continuous human oversight and access to your data for model training is no longer required. A tool like Fisent’s BizAI retains no data instead applying prompt engineering and state of the art LLMs without training on processed content. This pattern has a couple of positive implications:

Conclusion:

Content processing technologies have significantly evolved over time, with OCR and IDP providing foundational automation for structured and semi-structured documents. However, they fall short in fully automating processes involving unstructured or highly varied content. Applied GenAI Process Automation solutions, on the other hand, overcome these limitations by leveraging advanced models capable of interpreting and extracting the meaning from diverse content types, whether text, audio, or images. By adopting solutions like Fisent’s BizAI, businesses can not only boost efficiency and accuracy but also scale with future-proof technologies, ensuring continued process improvements and faster turnaround times.

Applied GenAI Process Automation solutions help effectively leverage the power of Foundational GenAI Models like OpenAI’s GPT, Google Gemini, Meta Llama, etc., as well as tuned LLMs to automate time-consuming repetitive enterprise tasks such as contract reviews, new customer onboarding, and order processing.

By transforming enterprise workflows, this new category of solutions helps drive efficiency, cut costs, and speed scalability while benefiting from the continuous improvements enabled by GenAI models.

Use of these systems is now happening rapidly and at scale. Applied GenAI Process Automation solutions should support a multiple LLMs and various hosts, handle high accuracy in content interpretation, and ensure data privacy among other things.

This paper focuses on the opportunities that GenAI models present for enterprise process automation and how best to put them to use towards productive and profitable ends.

Applied GenAI Process Automation: Harnessing the Power of Generative AI Models for Enterprise Process Automation

Anthropic’s recently released Claude 3.5 Sonnet has been making big waves in the GenAI community over the past few weeks. This model marks the most recent blow in the battle among model providers for the crown of most “intelligent” model. Anthropic’s updated Sonnet version boasts improved accuracy rates, a more capable UI, and comparable “intelligence” to the current industry king, GPT 4o. This release has once again reinforced Anthropic’s viability as a notable competitor to OpenAI. Yet more importantly, the release of Sonnet 3.5 is yet another demonstration of the ever-persistent trend of rapid GenAI model growth.

We touched on this topic briefly in our previous post when discussing ways to deal with models being quickly made obsolete by the drastic improvements of their newer counterparts. In mentioning the drastic speed of GenAI model improvement, we prompted the greater question: Exactly how fast are GenAI models actually improving? 

GenAI Growth Rate:

Taking Claude’s benchmarks as an example we can see material improvements, some as high as 11%, on key industry benchmarks in a matter of just three months. These marked improvements demonstrate Sonnet 3.5’s rapid increase in reasoning capabilities, specifically excelling at math problem solving and graduate-level reasoning. Important to note is that Anthropic delivered these significant improvements at a fifth of the price of their previous model, Claude 3 Opus, meaning they improved on all of the typically asymmetric metrics: speed, accuracy, and cost.  Yet, this level of iteration has become commonplace and even expected across the highly competitive market of model providers, with similar jumps in model capabilities in the newest Gemini and GPT model suites released in early May.

Benchmarks only tell part of the story, though. The growth rate of model training datasets provides a more quantitative perspective on the rate of model growth over a large timescale and makes identifying trends across a wide range of models easier. These training datasets form the knowledge base on which foundational models are built. Understandably, as these training datasets grow, the capabilities of the models grow in correlation.

Model training sets have consistently been growing at an exponential rate of 2.9x. To put this growth rate in perspective; Moore’s Law predicts that the number of transistors in a circuit will double approximately every two years, in comparison, over the same two-year period, AI model training datasets would grow by a factor of 8.4x. Since 2020 the average model training data set has grown by 70x. This trend has only become more pronounced since the release of the ubiquitous Transformer model, used in Gemini, Claude and GPT.

This growth rate can be expected to continue into the near future, with estimates of models encountering data scarcity sometime between 2026 and 2032, yet with the promise of processes like synthetic data generation even these dates may end up further delayed.

Where to Next?

With models set to continue growing at this rate, it’s easy to wonder where they may be going next. Intelligence, speed, and cost improvements are obviously all going to be key points of improvement. As OpenAI CEO Sam Altman put it simply, “GPT-5 is just going to be smarter.” Yet even the ‘smartest’ models are still limited by the effectiveness of their application. As a result there’s a growing need for tools that assist in effectively interfacing with these intelligent models to get the most effective outcomes.

This need can be seen being broached by a myriad of small startups but more importantly, can be seen being targeted effectively by the large model providers themselves. In recent model releases we’ve noticed that providers like Anthropic and OpenAI are placing a heavy emphasis on building consumer oriented features to assist in effectively interfacing with their models. In the May GPT 4o release we saw vision and voice functionality added to ChatGPT, with demos showing users able to talk directly to the model while also sharing their in person surroundings. More recently, in the aforementioned Claude 3.5 Sonnet release we saw the introduction of “Artifacts,” a new feature allowing users to generate, view, and edit content like code snippets or graphics in a dedicated workspace alongside their conversations with Claude.

Yet while this heavy emphasis on consumer centric tooling shows great promise for retail users it leaves a void for enterprises who are in a similar state of need for their use cases. We’ve seen massive consulting firms like Accenture capitalizing on this void, leaping to sell their services as the solution. An excerpt from Accenture’s Q3 2024 Earnings Call demonstrating the growth and mass of their GenAI practice:

With over $900 million in new GenAI bookings this quarter, we now have $2 billion in GenAI sales year to date, and we have also achieved $500 million in revenue year to date. This compares to approximately $300 million in sales and roughly $100 million in revenue from GenAI in FY23

Yet, model providers can’t be expected to roll out widely applicable tools to solve this void for all enterprises. In our significant experience integrating GenAI for our enterprise clients, we’ve found that each niche enterprise use case requires its own degree of finesse. And while consulting firms are one solution, even possibly a good one, they’re not the only option.

Effective Enterprise Tools: Fisent as an Example

At Fisent, we solve a very niche yet widespread enterprise issue: effective content ingestion. We apply the capabilities of GenAI within enterprise process automation software, augmenting the pre-existing workflows of a given enterprise to enable better experiences for their clients. Through our service we enable enterprises to have greater accuracy, auditability and speed in the processing and ingestion of content. By applying GenAI in this tailored manner, we are able to provide highly capable solutions that are personalized to our clients, unlike umbrella solutions that may eventually be rolled out by foundational model providers. Additionally, Fisent’s breadth of experience addressing this specific issue means that we have pre-built tools, processes, and endpoints that allow us to efficiently, and cost effectively integrate within any enterprise, unlike the more costly and time-intensive solutions offered by the large consulting firms.

Regardless of the method of application, it’s indisputable that GenAI is progressing rapidly. While retail users are benefiting from software  that makes applying GenAI easier than ever, it’s clear that enterprises can greatly benefit from more effective tooling. If you’re interested in learning more about Fisent’s GenAI Solution, consider reading our BizAI page. Alternatively, if you’re interested in learning how to evaluate the wide range of available LLMs, check out our previous post.

This post was also published on May 26, 2024 on LinkedIn

Over the past year, it has become increasingly clear that effective GenAI usage is a necessity for the modern enterprise. As a result of this paradigm shift, business leaders worldwide are grappling with the question: How do we make the best use of GenAI?

Among many other points an important part of the answer to this question centers around selecting the right AI model(s). At Fisent we’ve had the privilege of navigating this question with various Fortune 1000 companies. Throughout these implementations, we’ve arrived at two significant realizations. First, selecting the right “model” or “Large Language Model” (LLM) for a particular use case is never a one-size-fits-all proposition. And second, when integrating an LLM within an organization, companies can not rely solely on a ‘set and forget’ strategy. 

Selecting The Right LLM:

When selecting the right LLM, application context is incredibly important. What this means is that organizations must identify their organizational priorities and pick a model that aligns with these priorities. 

For example, take a retail organization using GenAI to interpret complex purchase orders to automate and accelerate order fulfillment. Due to the length of these complex purchase orders the retail organization would likely need a large context window. Additionally, the inherent cost to interpret purchase orders manually and a need for a high degree of accuracy might drive this organization to prioritize model accuracy over pricing. As a result this organization would likely consider using an expensive, high accuracy model, like Claude 3: Opus. 

On the other hand, take an energy provider planning to shorten customer wait times by using GenAI to accelerate the processing of a high volume of simple utility bills. In this example, the simplicity of these utility bills, the high-volume nature of the task, and the importance of speed would likely drive this organization toward a cheaper and quicker model like Llama 3.

These examples show how organizations can use published benchmarks and model statistics to evaluate the plethora of available models and identify the model(s) that align best with their needs. The table below shows some of the current industry leading LLMs and some details an enterprise may consider during  evaluation. 

	GPT-4o	Claude 3 Opus	Gemini 1.5 Pro	Llama 3 70B	Mistral Large
Context Window:	128k	200k	1M	8k	32k
Type:	Proprietary	Proprietary	Proprietary	Open Source	Proprietary
Native Input Types:	Audio, Image, Video, Text	Image, Text	Audio, Image, Video,Text	Text	Text
MMLU Benchmark	88.7%	86.8%	85.9%	82%	81.2%
HumanEval Benchmark (Python)	90.2%	84.9%	84.1%	81.7%	45.1%
Input Cost per 1M tokens	$5	$15	$7	$0.59	$4
Output Cost per 1M tokens	$15	$75	$21	$0.79	$12

Yet, while benchmarks and model statistics serve as a great initial guide it’s important to note that GenAI models can often perform in unexpected ways. These less tangible outcomes can occasionally be equally as important for an organization on their quest for the right model.

For example, some models easily react to prompts containing potentially ‘sensitive content’. So an organization interacting with information relating to dangerous chemicals may find that a model that statistically fits their requirements might fail to process their content because of flagged prompts. Organizations therefore still need to experiment with multiple models using realistic simulations to finalize the ideal choice. 

In short, a powerful framework enterprises should consider is as follows:

Staying Ahead of Change:

In the ever-changing technical landscape of GenAI, change truly is the only constant. In other words, the best model today will not be the best tomorrow. Just this past week, OpenAI announced the release of GPT-4o, setting a new standard for model multimodality and accuracy. As we mentioned in our last blog post, models have been making material improvements at a rapid rate, a trend we can expect to see continue. This, paired with the irregular release of new models from different providers, has resulted in what Groq CEO Jonathan Ross calls a “leapfrogging effect,” where there isn’t just one industry leader and the most effective models can change by the month.

What this all means is that it is important for organizations to pick their models purposefully but also be agile enough that they can swap to the best models as needed. At Fisent we believe in bringing simplicity and effectiveness to the lives of our clients. This means that we help select the best models for our clients and through BizAI’s native optionality we ensure that our clients are always using the latest and greatest models for their needs. 

With the release of Meta’s new open source LLM, Llama 3, on April 18th 2024, the tech community has been captivated, sharing stories about the power and potential that this model will have on the startup ecosystem. Some are speculating that this will eliminate billions of dollars in startup capital and valuations as there is now a viable open source competitor to OpenAI’s GPT-4. I don’t disagree that some startups, specifically those betting on building the next “GPT killer”, will find themselves increasingly falling behind the blistering pace of the open source community, especially with Meta investing billions in the space. However, as an operator actually deploying GenAI solutions in the enterprise ecosystem, I have a few different thoughts:

We are only weeks away from the highly anticipated release of OpenAI’s next GPT series model. Only 13 months have passed since OpenAI released GPT-4 and it has remained near the leading edge of performance since that time. Meta may have made a significant step in closing the gap for now, but I strongly suspect the next 12 months will continue to be owned by the closed source models. In a recent interview Sam Altman gave with Lex Friedman, he revealed his views on what is to come:

…relative to where we need to get to and where I believe we will get to, at the time of GPT-3, people are like, “Oh, this is amazing. This is marvel of technology.” And it is, it was. But now we have GPT-4 and look at GPT-3 and you’re like, “That’s unimaginably horrible.” I expect that the delta between 5 and 4 will be the same as between 4 and 3 and I think it is our job to live a few years in the future and remember that the tools we have now are going to kind of suck looking backwards at them and that’s how we make sure the future is better.

At Fisent Technologies, we’ve been architecting BizAI with a focus on the enterprise since day 1. That focus is why we took the approach of not making a bet on a single LLM. Instead, by design, we support a capability we call multi-model, multi-host, which gives total optionality to our clients to use the right model for the job, regardless of where they are hosted. This also means as the next most powerful model becomes available, our clients can seamlessly transition to using that model to power their underlying automations, taking advantage of material, instantaneous improvements in reliability, speed and accuracy.

As actual practitioners in the enterprise space, the number one concern we come across is the accuracy and reliability of these models when applied to specific business challenges. That is why the models and services that can deliver the most accurate and consistent results will continue to win at the enterprise level, while the competition, even if close behind, will continue to find themselves relegated to the bench.

Innovation is the key to growth. In the right doses and with the right timing it can be incredibly powerful and profitable in competitive markets. There is no question that innovation comes with its own set of challenges and risks, which is why innovation is a critical component for successful long term risk management strategies.

Financial institutions have to overcome many technical roadblocks that can impede their innovation goals, especially as they navigate the compliance landscape. Whichever route they choose, the end result must be secure, responsive, and accurate.

A lot of clients often feel as if financial institutions can only pick one or two of the aforementioned three attributes. Software can be accurate and secure, but responsiveness lags. However, with the right risk management tools, financial institutions can have it all.

The Challenge of Innovation

Financial institutions are constantly trying to leverage the latest and greatest technology, aiming to make their customer and user experiences easier and more efficient. From digital identity solutions, biometrics and decisioning tools to name a few, there are simply too many tools to evaluate in the market . To make matters even more challenging, there are new innovative fintech solutions coming out almost every month.

So how are financial institutions able to innovate quickly and thoughtfully with a bounty of choices and outcomes to consider?

No institution is going to shift from one technology solution to the next and completely change their processes every time. Organizations need to have designed a flexible enough system design so that when new technologies are released into the market, they can easily leverage that new technology without having to rebuild large portions of their technology stack. Inflexibility is the antithesis of being able to innovate and keep up with technological change.

Institutions are faced with an even bigger challenge when trying to innovate around background, or supporting processes. Often these underlying processes do not change fundamentally, either because they are designed for specific reasons, such as regulation, or the change management process around the policies themselves is very bureaucratic within the organization. What does often change are the tools that are being used to facilitate or manage that process. Adapting to those changes leads to more change management, which in itself may be manageable, but left unchecked over several innovative cycles can leave users overwhelmed and frustrated, having to constantly re-learn a new process.

Deciding what is useful can also be a challenge. After all, there are a lot of buzzwords being used—AI, machine learning, neural networks. These terms sound great and intriguing, but what specifically can they deliver to financial institutions? There are certainly ways that AI and machine learning can benefit financial institutions, but every organization has to ask itself two important questions: why should they implement these services and how they will be implemented. Institutions that cannot answer these basic questions will find themselves with very expensive and lengthy implementation projects that ultimately do not deliver on their desired business outcomes.

By leveraging low-code workflow and process automation platforms, such as Pega, organizations can ‘plug and play’ and see which tools within a system can work for their process. After a short period of time, an organization might feel that that a new software solution is not delivering the value they anticipated. At that point, if they are using a platform, removing or replacing the tool (and even testing out a different one) is much easier than working within a system that requires traditional programming. Organizations don’t need to rewrite part of a business process to accommodate a new technology, rather the technology supports the process and can be as flexible as needed over a longer periods.

Aside from technology, factors such as competition and regulatory restrictions can create friction to the innovation process. Organizations can reframe these challenges in a transformative way by enabling change through software.

As financial institutions change and grow, they need to have the flexibility to integrate future innovations. After all, a financial institution cannot just start from scratch every time they switch systems or undergo a major upgrade. Financial institutions need to have foundational infrastructure through which they can plug and play specific end-point technologies. This flexibility is paramount in order to encourage innovation while enabling employees to be the most productive.

The Innovation Investment

Innovation is often expensive and adopting new systems can be risky. There is no guarantee that the developers of some of these software solutions will be around forever. This uncertainty can be paralyzing and prevent organizations from adopting change. On the other hand, some of the most innovative solutions are coming from the newest entrants to the market. Innovation for innovation’s sake does not help customers and can cause confusion and headaches. Adopting tools that do not integrate well into an existing process can create technical debt as teams work to fix problems, not to mention the upfront and possible recurring costs of services.

Finally, there is a risk with innovation focused around specific solutions, as opposed to business outcomes. New technologies might do a very good job at generating interesting data points, but this data may not ultimately translate to an improved bottom line. In a silo, data does not always provide value. Value is created when data is layered with the skilled interpretation of that data and the knowledge to understand how to process it. The result is actionable intelligence. Many of the fintech solutions available today provide a wealth of information, but until institutions can harness that data, they will be inundated with information that is not driving to their business outcomes. Especially in a regulated arena such as financial services, not understanding information can create new risks, as receiving alerts and warnings, but not understanding them, can lead to liability.

Despite some of these risks, innovation is still worth the investment and there many existential risks to not innovating. No one can stop progress, and the danger of not innovating is to become redundant and ultimately relegated to history.

New technologies and innovations also present important new features that can greatly improve the quality of service for financial institutions. Not innovating means that competitors can adopt these solutions and services first and gain a competitive edge in an industry that is already extremely competitive. While there is risk in being a early-adopter, there is always a risk, potentially an even greater one, of being left behind.

The Right Way to Innovate

Clearly, there are different paths to innovation, but innovating in the wrong direction can cause operational stress and financial pain. So what do organizations look for as they innovate? How does the innovation process make organizations better?

First, it is critical that institutions have the capability to leverage, in an efficient manner, many different technologies if they want to truly improve business processes. Organizations want to make sure that whatever they implement integrates with an existing system or framework; otherwise, they are going to struggle with compatibility issues and adoption.

The other reason organizations want flexibility is to reduce dependencies on any one external provider. While most of the focus in an innovation context is on adding services and features, removing some of those services can also be a costly endeavor. To reap the benefits of innovation in the short and long term, institutions should seek to “future-proof”, by creating a system with the capability to both accept new solutions and remove those same solutions as required.

Finding the Balance

Innovation ultimately needs to benefit the customer experience, and the latest innovations for financial institutions bring powerful options for customers.

Of course, innovation carries its own risk, but just like every risk, there is a downside and an upside. Finding the right innovation that clicks with your business is a risk that you should be pursuing, not avoiding.

If you’re interested in learning more about staying on top of the latest innovations in the Financial Services landscape, check out our on-demand webinar, “How FinTech is Evolving in 2022 and Beyond”. We dive deeper into the topics discussed in this blog post and discuss how organizations can make smart investments that will provide a “future-proof” system.

Financial institutions are in the business of managing risk. Whether evaluating a loan application or analyzing investments, every decision is made with respect towards the bottom line. However, smart risk management approaches consider all aspects of risk including those that could threaten operations, client onboarding and assessment, compliance and legal liabilities, financial uncertainties, and technology. Left unchecked, institutions run the risk of damaging an otherwise good reputation. By practicing a holistic, ongoing approach to managing risk, institutions can yield better financial and business outcomes.

While financial institutions all face similar risks, their processes for evaluating and assessing risk can vary greatly. Yet, all risk assessment processes are not created equal, and some methods can leave institutions vulnerable to missing key factors that can cause them to approve the wrong customer or be slapped with fines for non-compliance.

At Hoverstate, we speak with many different financial institutions about their risk management processes. We are experts in the challenges they face, and what an ideal state looks like. To truly have an effective, streamlined risk process, institutions need to rethink their approach to risk management.

Challenges with Financial Risk Management

Risk management has only become more complex in recent years. Risk teams are under immense pressure to increase efficiencies, modernize technology, implement processes, evaluate procedures, and remain compliant with regulations.

Here are some common challenges institutions face with their risk management processes:

The Role of Technology & Automation in Financial Risk Management

To help institutions combat the clear challenges that come with streamlining and scaling the risk management process, a slew of tools have hit the market in recent years promising to help ease the burden of managing the process itself. With the right digital infrastructure in place, financial institutions can flourish by understanding data faster and making better risk decisions with ease.

Digitization tools can break down silos and connect people to one another while also removing bias that can cause flawed decisioning. If analysts are having to spend more time reading and consuming emails instead of looking data, or if people are finding themselves doing tasks that are not directly contributing to scalable growth, a fluid, flexible risk management tool can help remove the stand-alone, mundane work environment that existed with a more manual process.

Most importantly, digital tools are more consistent and reliable at capturing documentation and information right where it needs to live, which helps to protect your institution against avoidable legal claims. Imagine a bank with five or six different fraud risk detection engines running during their onboarding process, but each of them outputs a varying range of scores and continuous alerts.

While helpful and powerful information is being generated, institutions need to have the technology in place to organize this data into a process or establish a way of reviewing it effectively and at scale. Without this ability to manage data at scale the institution may be at increased risk because they can be shown to have known about issues, but not properly addressed them. From a regulatory perspective that lack of connection between the awareness and the action taken can seriously damage an institution.

Essentially, risk management technology plays a key role in improving organization-wide activities. Better communication means the less likely details become lost from person-to-person and step-to-step in the process. Instead, when questions arise, information is easily located and disseminated, allowing for thoughtful and confident decisioning. Over time as processes rinse and repeat, a layer of automation alleviates the amount of time spent in mundane – yet critical – tasks, so that the focus remains on growth and scalability rather than being continuously stuck in a spin cycle.

Even though digitization is common in our modernized world, not all financial risk management technology addresses the challenges with thoughtful solutions, so we encourage financial institutions to think about their organizational values and select a technology that both aligns with their values and can be flexible as needs change.

Challenges with Risk Management Technology

When it comes to truly managing and mitigating risk, your effectiveness is only as strong as your processes. If the process or steps are not clearly defined for problem resolution, all you are doing is revealing fundamental issues without solutions.

Of course, simply having shiny tools will not solve all problems. Many different tools and technologies promise the world while not actually delivering any tangible value or are too overly rigid that they end up creating more work instead of less. When organizations run into problems with one tool, they often supplement the weak areas with a different tool. This compounding effect further exacerbates the problem as even more types of alerts are now coming from different tools, causing a phenomenon commonly known as “alarm fatigue” – meaning you are so used to the alerts they almost fade into the background.

As alerts compound, it becomes more likely that alerts are not making it to the right people at the right time. And because an institution was alerted yet failed to act, it can leave them more liable than if they did not have a risk management tool at all. A system does not need to exist simply as an overwhelming alert machine. With the right programming, a good system will both create alerts for perceived issues and trigger an automated process that resolves the root cause for your alert.

The other challenge with digitized systems is that they can become overly rigid. Rigidity can be positive when a process is really strongly defined. However, the challenge with risk assessment processes in financial institutions is that each have their own lens. Regulation, compliance and risk assessment matters necessarily have subjectivity and as such, each institution has their own way of looking at and managing risk. This could be because of jurisdiction matters, customer types, products and services offered, the type of organization, the risk appetite from the board, the types of employees, the culture – all of these change your way of doing business. This wide range of differences is part of what drives the variation in digital technologies. However, these systems often become rigid and people get locked into thinking that a particular process must be done this way. By introducing that little bit of flexibility through technology institutions can shape the process to fit their specific and ever-changing needs.

Risk Management: Finding the Balance

In the same way it is important to understand the level of risk an institution is willing to take on, it is equally important to understand the limitations of digitized systems in that risk assessment process. There is no silver bullet that fixes the process completely due in part to the fact that the very process of risk management has a dizzying number of factors. Even if such a system were to exist, would it work for everyone? Limitation can, in certain cases, turn out to be a good thing, but there are instances where a financial institution wants to take that huge, life-changing risk – and that can come down to having the right digital technologies to better assess, act, and manage risk successfully.

As with everything else in the financial services space, risk management will continue to evolve as consumer behavior and new technologies hit the market. It’s critical for leaders to recognize these shifts and adapt their strategy accordingly to stay competitive.

WOODLAND HILLS, CA – Hoverstate, digital transformation firm, and Fisent Technologies, a financial technology software company, have announced they are joining forces to accelerate digital transformation in the financial sector.

Through this alliance, Hoverstate will be working with Fisent’s application, Fisent Risk, which allows financial institutions to design and configure their own proprietary risk engine, as well as automate their risk assessment processes. The application is designed to create transparency, automate real-time decisioning, and eliminate legacy systems, which compliments Hoverstate’s existing offerings to digitally transform client’s businesses with technology.

Fisent Risk is built on Pegasystems low-code platform, utilizing Pega’s industry leading intelligent automation and case management capabilities to create an end-to-end risk lifecycle management process. As a Pega Venture partner, Hoverstate’s expertise and track record in the Pega ecosystem makes them a well-equipped partner to help clients implement and use Fisent Risk within their organization.

“Hoverstate is excited to begin this partnership with Fisent Technologies.” shared Lowell Gilvin, VP Partnerships and Alliances at Hoverstate. “We believe the application will quickly transform risk management for established enterprises, as well as emerging markets companies by providing real-time analytics to better understand and manage risks.”

As the FinTech landscape continues to evolve at a rapid pace, both Hoverstate and Fisent Technologies aim to empower organizations to innovate and effect change with more efficiency and confidence through this partnership.

“Hoverstate is a leading partner in the Pega ecosystem, with a proven track record of delivering complex projects and creating exceptional value for customers.” says Adrian Murray, Founder of Fisent Technologies. “We look forward to partnering with them to bring Fisent Risk to the market, enabling more financial institutions to further digitize, automate and streamline their customer risk management processes.”