Author: Jiffy Admin

Why Wealth Firms Must Rethink the First Mile of the Client Journey

When a potential investor engages with your firm for the first time, you’re not just opening an account—you’re setting the tone for the entire client relationship. In the highly competitive wealth management industry today, digital onboarding is no longer a “nice to have.” It’s a strategic differentiator that shapes how investors perceive your brand, your service, and your commitment to their financial goals.

Yet, in many firms today, onboarding is still a complex, paper-heavy, and painfully slow process—often taking days or even weeks to complete. For digital-first investors who are used to near-instant experiences in other industries, this friction is more than just frustrating—it’s a deal breaker.

The Digital Investor Has Changed. Has Your Onboarding Process?

When it comes to building investor trust and loyalty, the first few steps matter the most—in this case, those steps involve onboarding.

Today’s investors are digital natives. They are financially aware and expect more than just transactions. They seek personalized guidance, education, and proactive insights from their advisors—starting from the very first interaction.

They compare your onboarding process to their best experiences with streaming services, mobile banks, and e-commerce platforms. They expect a fast, intuitive, and secure onboarding experience. They’re not walking into branches—they’re tapping into apps, clicking through portals, and expecting answers in real time. 

According to recent studies, 42% of wealth and asset management clients now initiate relationships through a website or app, yet 62% of executives admit their digital experiences still fall short.

If onboarding is clunky, slow, or paper-heavy, these investors are likely to drop off before the relationship even begins. 

Where Traditional Onboarding Falls Short

…(and costs you more than just time).

Let’s face it: onboarding in wealth management has traditionally been a manual, compliance-heavy, and fragmented process. Forms go back and forth, documents are uploaded and re-uploaded, and valuable advisory time is lost in getting signatures or clarifying KYC details.

Legacy systems weren’t built for today’s demands. Whether it’s onboarding different client types, handling various registration types, or enabling multi-account onboarding with multiple owners, wealth firms often rely on fragmented processes that require manual input, lengthy document reviews, and physical signatures.

Advisors often spend valuable time chasing paperwork instead of building relationships. And clients? They’re stuck navigating confusing instructions, redundant form-filling, or waiting days for verification.

The result? Friction, drop-offs, and missed opportunities.

The Case for Client Onboarding Transformation

Client onboarding is no longer just an administrative step—it’s a strategic differentiator. A well-designed onboarding journey can drive faster time-to-value, enhance client satisfaction, and improve operational efficiency across the front and back office.

To meet rising expectations, wealth management firms must enable:

• Frictionless client onboarding experiences across devices and channels
• Intelligent automation that supports pre-built client onboarding validations
• Streamlined client onboarding documentation and approval workflows
• Smart tools like AI-led document extraction, and real-time KYC & ID verification
• Flexible support for seamless document upload, e-sign, or even wet-sign as needed
• Smooth integration with account funding options to start the relationship faster

AI Is Reshaping the First Mile

Artificial intelligence (AI) is fast becoming a game-changer in onboarding. By automating data capture, document classification, verification, and compliance checks, AI allows wealth firms and financial advisors to focus more on advising—and less on administration.

And it’s not just about speed. AI also makes personalization scalable too. A seamless AI-powered digital onboarding experience doesn’t just convert prospects faster—it builds trust from the first interaction. When you get onboarding right, it creates a ripple effect across the investor lifecycle—from stronger retention to higher share of wallet.

Why Now Is the Time to Act

With growing client expectations, market volatility, and increasing regulatory scrutiny, there has never been a better time to rethink onboarding. And the good news? The technology is ready. Wealth firms now have access to pre-integrated solutions with embedded AI that make client onboarding transformation not just possible—but practical.

Want to See the Full Picture?

We’ve unpacked the trends, challenges, and AI-powered solutions shaping the future of investor onboarding in our latest point-of-view article: 

“AI-Driven Investor Onboarding: The First Step to a Delightful Client Journey.”

Download the full article now to explore how your firm can turn first impressions into lasting value.

[Read the Article]

Does your enterprise deal with an army of suppliers regularly? Is significant team effort spent on reviewing and sorting a multitude of invoices, but you still end up with late or erroneous payments?

Time to take a look at how JIFFY.ai’s Automated Invoice Processing HyperApp can help you save more by making the right payments at the right time.

The Hassles of Manual Invoice Processing

Any manual process is by its nature more tedious and error-prone than an automated solution. The incremental issues that arise when invoice payments are delayed or inaccurate can lead to severe issues in partner relationships and even break the supply chain.

Manual processing is costly and drawn-out and often fraught with a lot of duplicate and erroneous entries. The enterprise is left dealing with collateral damage in many ways:

• Strain in supplier relationships and a threat to company reputation
• Loss of purchase discounts in the invoice payment process
• Inclusion of late penalties, increasing the costs
• Rework to correct the errors adds to the processing time and costs more to the company
• Erroneous invoices may take weeks to straighten out with the suppliers and hamper the end-of-month closure of accounts

Need Of The Hour – An Intelligent Automated Invoice Processing System

According to AQPC’s Open Standards Benchmarking Accounts Payable 2020 survey, on average top-performing companies report that nearly 0.8% of their annual disbursements are duplicate or erroneous. On the other hand, bottom performers report more than twice the amount, at 2% of total annual payments. Just look at this in light of your yearly invoice payment numbers, and it will be staggering enough to take a real relook at the process. 

An automated invoice processing system helps in cultivating positive vendor and supplier relations. It enables users to maintain accurate records and respond to invoices in a timely fashion while ensuring prompt payments and precise records of supplier relationships. 

RPA-Enabled Vs JIFFY.ai’s Intelligent HyperApp Invoice Automation

RPA can quickly automate repetitive tasks in the invoice process and works on a rule-based approach. You can specify rules to flag exceptions when certain conditions are met and raise a request to the human agent to resolve the issue before the payment is approved. An intelligent automation system applies NLP and Machine Learning (ML) on top of RPA extending RPA’s ability to provide substantial augmentation. 

Our HyperApp Automation solution builds on RPA’s capabilities to ensure the invoice is accurate before being sent for payment.

• ML examines data captured in different fields and tries to establish a mapping between fields that hold the same pattern of data values
• Robust in handling the cognitive 3-way match between the Purchase Order, report of goods received, and the invoice
• Helps to check if the invoice is accurate, is not a duplicate and the invoice corresponds to the goods requested and received
• Can learn as fast and accurately as experienced humans in identifying and interacting with suppliers, automatically performing input intake, coding, processing and routing invoice workflows, denoting payment deadlines, approval workflows and approvers. It requires human interaction only at critical checkpoints.
• Makes it easy for supplier business users to interact directly with the invoice process through easy to use interfaces
• Provides the analytic ability to detect payment schedules well in advance, to reduce errors, and to save costs by incorporating purchase discounts in the invoice process
• Improves cash flow transparency with the validation engine confirming the accuracy of invoices before pushing them into the ERP system and reducing the need for downstream updates

Invoice Right on Time with JIFFY.ai’s HyperApp

JIFFY.ai’s HyperApp’s unique features for accurate invoice processing are:

• Intelligent Invoice Extraction – with built-in cognitive capabilities to handle complex invoices like line items, tables, etc. and the cognitive capability can automatically create templates for new invoices
• Customizable Supplier Portal – exercises full control over portal customizations to ease supplier onboarding and ongoing invoice management
• Configurable Workflows – allows faster implementation cycles, additions of new suppliers or new product lines, configurable for a multitude of suppliers and invoice types
• Powerful Validation Engine – ensures all validation is handled ahead of ERP to avoid downstream updates in the ERP system
• Powerful Data Analytics – the underlying data layer allows us to provide analytics around the process itself and also around business intelligence
• Pluggable ERP connectors – can plug directly into your existing infrastructure

Impactful ‘App’lications

Many organizations have reported reduced errors and improved overall process efficiency after implementing the JIFFY.ai HyperApp solution for their invoice process. 

Statistics:

Error reduction – 90%+

Efficiency Improvement – 85%+

Intelligent automation of the complex invoice process with a more collaborative and connected approach and smarter, dynamic data-driven decision-making ability is required in today’s hyper-competitive, fast-paced business landscape. The JIFFY.ai HyperApp solution is the right choice for you to transform your invoice process, increase your revenue and maintain trust with your suppliers.

We are very proud to be named a gold medalist and to claim the #2 provider spot overall in the SoftwareReviews 2020 Robotic Process Automation Data Quadrant Report. The report studies over 40+ parameters across three main dimensions, including product features, vendor capabilities, and the relationship clients have with their software vendor.

A division of Info-Tech Research Group, SoftwareReviews recognizes outstanding vendors in the technology marketplace as evaluated by their users annually. The report is unique because it studies not just internal and operation parameters but also client sentiment around the product. JIFFY.ai ranks consistently high on customer satisfaction, product support, and more.

We are incredibly grateful to our customers for sharing their experiences and reviews, which helped us rank #1 in the Emotional Footprint scoring where we achieved a Positive Net Emotional Footprint of 97%.

“Selecting top software vendors is becoming an increasingly transparent and data led process” says David Piazza, President of SoftwareReviews. “SoftwareReviews Data Quadrant provides a total view of the performance of a software vendor, from core features and capabilities to the important client-vendor relationship, what we call the Emotional Footprint. Vendors who have the strongest Net Emotional Footprint in the category demonstrate they have been successful at building strong relationships with their customers.”

Client sentiment is fundamental to us and we thank everyone who has invested time and effort in providing their feedback.

At JIFFY.ai, we believe automation accelerates innovation and we have the singular vision of putting the power of automation in business users’ hands. Our product strategy and roadmap are driven by this philosophy, which has helped to drive our first-place ranking in categories such as Native AI and Automation Apps and our second place ranking in business-level parameters such as Product Strategy and Rate of Improvement.

I would also like to highlight the Automation Apps Differentiating Feature category of the report where JIFFY.ai outranks all competition. Our HyperApps are focused on end-to-end automation of complex business processes and on making these automations quick to implement, resilient, and easy to maintain. We uniquely combine business process management, cognitive automation, and low code/no code development into a single platform to achieve lower total cost of ownership for our customers. To be ranked first on this parameter is exciting and reinforces our strategic leadership in this area of enterprise automation.

With much gratitude and appreciation to our clients and to the JIFFY.ai team, we look forward to continuing the work of helping our clients innovate through automation. Please find the full copy of the SoftwareReviews report here. https://jiffy.ai/resources/reports/rpa-softwarereviews/

How to build efficiency using intelligent automation

When we think of Robotic Process Automation (RPA) in procurement, we know that adoption is already on the rise. Many businesses are using RPA in their value chain, and for those that aren’t yet, it is a factor of ‘when’ and not ‘if’ they will use RPA at some point in time.

In a domain as complex as procurement, RPA ensures that most tasks and processes are automated at a fraction of the cost of adding headcount/resources or deploying new teams. The benefit in addition to using a computational system is also being able to work around-the-clock (thanks to RPA), which  significantly reduces dependencies on human resources.

The true value of RPA is being able to repeat complex tasks and follow decision trees effectively. But with machine learning, cognitive processing, and natural language processing gaining traction and advancing at an accelerated pace, it is only natural to integrate this with RPA to deliver a more effective solution across the value chain.

Enter intelligent automation.

Now let’s dive deeper into why machine learning, cognitive processing, natural language processing, analytics and RPA must go hand-in-hand, and how learning algorithms coupled with RPA’s execution capabilities are the future of full automation.

What is cognitive procurement?

In the field of supply chain automation, cognitive procurement refers to the process of using automation with machine learning, analytics, and other cutting-edge technologies to help automate further, faster, and more efficiently.

Procurement as a process is characterized by large amounts of unstructured data, which may be impossible to process using traditional systems.

Apart from solving the problem of unstructured data handling, cognitive procurement also helps:

• Transform all existing purchase and transfer order systems, sometimes entirely
• Transform supplier onboarding and the associated processes with automation
• Forecast prices and inventory needs, create reports with usable data and power better decision-making
• Conduct risk assessment to prepare for known threats to the value chain

The best part? A cognitive procurement solution can also connect to external sources of data and tie these parameters into the recommendations it makes. RPA alone may not be able to do so, but when supported with the right data and learning systems, the possibilities are nearly endless in the space of procurement.

Intelligent RPA and its role in cognitive procurement

Cognitive procurement is often referred to as the final frontier in the procurement process. However, wisdom and experience show us that most of the quantum of human knowledge is actually ahead of us. In the era of information, we need a system that can handle three aspects of any complex task:

• Research and data processing: This is where analytics come into the picture.
• Learning from past data to make accurate predictions for the future: Machine Learning works on the principle that when an artificially intelligent system is given enough data to work with, it can make decisions that are as good as, or better than, their human counterpart.
• Execution: Any plan is only as good as its implementation, and the sheer volume of work and number of branches in the process. Post-machine learning interventions need RPA to help in seamless execution.

As a final product, businesses with a vast and demanding procurement function can expect to achieve efficiency in analyzing their data, manage their supply risk, procure and pause material based on real-time needs, plan logistics for better efficiency and optimized costs, evaluate their suppliers based on their monthly, quarterly or annual performance across as many parameters as needed, and provide 24X7 support throughout.

Why should you implement an intelligent RPA solution in procurement?

According to a KPMG research report, “Delivering value in procurement with Robotic Process Automation,” implementing intelligent RPA can deliver over 50% savings in procurement costs, increase Return on Investment (ROI) by 5X, and reduce the number of strategic suppliers needed by nearly 50%.

How should businesses decide where and how to implement RPA in their procurement process?

Start by reviewing existing procurement processes to identify areas where the scope for automation is high. These tasks often represent repetitive actions that offer less value per extra time unit spent.

However, for an RPA system to work, the process needs to have a clear workflow and lead to non-ambiguous outcomes. Technical specifications include processes that run in relatively stable environments, and cases where manual intervention to solve for an impasse can be kept low.

Next, identify these processes based on how much business impact using automation could create, and how much effort might be necessary to implement RPA in this process. With these features in mind, the tasks can be classified into low-impact, low-effort-to-implement processes which make for good early adoption and trial cases, and high-impact, high-effort-to-implement processes which can effectively transform the business.

As a process laden with numbers and data, procurement presents the best use-case for implementing RPA in tandem with data analytics and machine learning. Companies that have already done so report unprecedented results across crucial parameters. One of the barriers for RPA implementation is worry around the cost-to-benefit ratio, which these numbers quickly disprove. The next barrier is a fear of ‘machines taking over the world’, which in cases as complex as a global supply chain – may be a good thing, as the pandemic’s disruption to this key process has shown.

The human capital that has been freed from the clutches of repetitive tasks and handling data too complex to process, can now be used in functions needing more human intervention and creativity. This leaves the machines to do what they do best – repeat every process error-free, follow the rules and use data effectively.

Have you ever said, “Let’s start small and then build it up based on how it goes,”? You sure have. So have most of us. In our world, this is typically how all automation begins.

During the initial days of robotic process automation (RPA), organizations were mostly skeptical. They saw potential but were unsure of real impact.

So, they tried it out for small non-critical functions — they wanted to minimize risks. Understandably. Say, the finance department would automate one task in the Accounts Payable first such as reading data from a file and transferring that to the ERP system. However, other aspects of the Accounts Payable process would continue to remain manual. Also, understandable.

This is what is called partial automation — quite literally, automating just a part of something much bigger.

But why would anyone do that?

In fact, there are plenty of reasons for handling automation this way.

For one, the earliest automation systems could only automate basic screen capture – in other words, anything that couldn’t be seen on a screen would break the process and need manual intervention.

Some of them are financial — end-to-end automation is more expensive and incurs higher opportunity costs to run business-as-usual in the interim because every sub-task would need investment in a bot. Partial automation, on the other hand, was cheaper. Organizations could pick a few bots for shorter processes and pay-as-they-go. This also helped them understand the effectiveness of automating and calculate ROI in the longer term.

Some industries worried about security. A bank would use RPA tools to move data from a front-end system to a legacy back-end system but wouldn’t let bots analyze their customer data. Even to this day, security remains an important reason companies choose partial automation. Why risk exposing critical data while their mandate – bolstered by regulatory requirements – is to protect it and keep it confidential?

Some others just weren’t ready for end-to-end RPA — automating a process end-to-end would necessitate standardization of formats, fields and rights, and that requires an investment of finances, as well as time and energy from their internal teams.

It also didn’t help that monitoring each automated process or bot was not easy. So, there was greater risk of broken automation if the scope was end-to end.

The initial RPA landscape had its limitations, lacking seamless integration with the human input when the time came for decision-making and without a human-in-the loop concept.

Most also feared that they might not have the people trained and equipped to intervene and improve the end-to-end RPA, making it a bigger risk. Partial automation is less demanding.

To be clear, in all these cases organizations certainly understood the value of RPA, invested in partial automation and derived value from it. Most of them are “somewhat happy” with the results their RPA systems are delivering.

Partial automation only provides partial success. Why?

Process measurement issues: Partial automation meant that a major part of the processes still had to be done manually, so there was no way to measure the ROI per process or per team/department. In other words, there was no way to make a strong case for automation because the results couldn’t be measured objectively.

Efficiency deficit: The improvement in overall process efficiency, while automating only a part of it, can often be so minimal it doesn’t seem worth the effort.

Savings deficit: As efficiency is only marginally improved, cost savings also end up being marginal.

Stagnation: Partial automation can be a dead investment without the bot’s ability to learn, adapt or grow with the needs of the organization. Likewise, it can be a dead investment if the organization doesn’t have the ability to see and manage how automation is being applied across the enterprise.

Resource blocking: Without the ability to improve intelligently, partial automation still needs people to fill its gaps. This means that people continue to work on mundane tasks, leading to low productivity, fatigue and dissatisfaction.

Right, so is Intelligent Automation a possible end-to-end solution?

Intelligent or Cognitive Automation in its simplest form, is an intelligent version of RPA — one that can learn from the data and apply it to present needs. Automation can become limiting when not supported by the learning capabilities of AI, which is where intelligent automation comes into the picture. It is flexible enough to understand and adapt to non-templatized data inputs. It can process structured, semi-structured and unstructured information with ease.

Take Jiffy.ai’s cognitive automation tool, for instance. It is able to read and extract non-templatized information. Even in cases where Jiffy.ai doesn’t understand or cannot read certain parts of the document, it will extract all the other parts and reduce manual intervention to a bare minimum. This way, with cognitive RPA, you can automate the entire process, not just a part of it.

With its ability to learn, cognitive RPA is also scalable. As a business becomes more complex and processes more intricate, cognitive RPA can learn and grow along, making the ROI significant in the long term. For instance, intelligent automation systems that trigger alerts to floor supervisors in a manufacturing unit can learn to spot newer anomalies over time, making all aspects of productivity, quality and capacity predictable. Enterprises are addressing their requirement for end-to end automation using a combination of RPA tools (for repetitive tasks), BPM tools (for process management), OCR , IDP tools (for document extraction), Data platforms for data streaming and beyond.

Instead, a platform that makes all of these features available in a single stack can help save costs and time, and also translate to easily calculable returns over a period of time. This way, they can adopt cognitive RPA for all processes, interconnect them and enable them to work in tandem.

Cognitive RPA also comes with basic skills. Pre-built RPA systems, customized for industries and functions, are now available with the ability to hit the ground running immediately. Once installed, they are in auto-pilot mode needing very little help from people, even for setup, training or maintenance.

With prior knowledge, pre-built cognitive RPA solutions can automate end-to-end with a more meaningful understanding of the process landscape.

With cognitive RPA, the solution is no longer piecemeal. Unlike partial automation, cognitive automation impacts the entire value chain.

Today’s context

The global situation businesses face today is a reason for organizations to take seriously how end-to-end automation can help them to be more resilient in the face of crisis.

As an example, a large automaker based out of Europe has worked with Jiffy.ai in automating their financial processes. This truly helped them recently when there was no business shutdown in their country, and they continued to send in their documentation to Jiffy.ai’s offices where physical offices were shut down. Thanks to automation, backend support continued seamlessly while production continued as planned.

It is completely understandable if you have a partially automated system now. It made sense in its day. But today, to see the real value of automation, end-to-end cognitive automation is the way to go. With a clear view of the entire system, end-to-end RPA will be able to bring together various processes into a smoother journey, be it for your customers, vendors or employees. It will also future-proof you as the system understands your existing processes and can expand to accommodate newer ones.

If you have adopted partial automation and aren’t fully realizing its potential, speak to one of our consultants to explore newer avenues. We understand where you are and we’re happy to help.