Assuring enterprise level data security – A critical aspect in your automation program

With great power comes great responsibility.

Nothing could be a better metaphor when it comes to the vast volume of data that organizations are in possession of today. However, with it comes the added responsibility of securing this data and ensuring that it does not fall into wrong hands.

As organizations evolve in how they serve their customers, automation is becoming increasingly critical to businesses. Repetitive processes and tasks are being automated to ensure speed and efficacy. Bots are replacing humans in tasks that require minimum diligence and more accuracy.

As all these actions play out, one factor that is continuing to grapple organizations is the security of critical data. As automation is done at scale, it deals with highly confidential data, which is transferred from systems, and processed across different verticals and accounts using individual passwords. This makes the automation platform prone to risk, as it has access to all kinds of confidential information such as financial statements, payrolls, invoices, deals and other information about clients, customers, and employees.

Implementing a secure automation warrants certain controls to be put in place during the automation journey. Identifying the right automation platform that takes into account the security aspects, is a key task for the architect at the design stage itself. One of the most frequent yet critical issue is that the digital workers are often run under the credentials of a human worker. This leads to a traceability problem and reduces audit worthiness of the entire workflow. A unique identity and separate access for the bots is a must to ensure that tracking and auditing the process is efficient. Without this, it becomes easy to do bot hijacking, or other manual proxy actions carried out by humans under the identity of bots.

A typical automation process undergoes multiple stages in its lifecycle, and it becomes essential to embed relevant security controls at all stages to ensure a secure process. Whether it is process discovery, design, build, validation or deployment, an ideal automation platform should be well-equipped to address the security issues at each stage and ensure a robust process to deliver a seamless experience in the long run.

Some of the key aspects that need to be addressed in this regard are:

Secure engineering:

Firstly, ensure that the RPA software itself is engineered using secure SDLC methodology with mandatory security assessments including SAST, DAST and manual penetration tests. It is also desirable that there are periodic independent third-party penetration tests and audits conducted to eliminate any security threats. The software should also support at least AES256 encryption for storing the robot’s own login credentials and target application login credentials. Further, the encryption key should also be encrypted with KEK (Key Encryption Key). The RPA software’s integration with other enterprise security vendors such as CyberArk, Windows Credential Vault etc. is also an essential part. For data in motion and in rest, the software should support TLS1.2 and AES256 at a minimum.

Enabling traceability and auditability:

Continuous supervision is a critical aspect to ensure prevention. Choose a platform that provides complete audit logs, which enable tracing and recording of every action the robots and the users perform within the automation process. Traceability and auditability are key to root cause analysis or initiate retrospective investigation into issues – both techno-functional and security related. The software should provide adequate transaction logging during process execution and user-friendly interfaces to inspect the transactions to enable easy traceability.

A secure design:

Some of the best practices for making the automation software secure is by following a secure design framework with a threat model created after defining the threat surface, identifying the threat actors and potential weak links.

The design process itself should include:

  1. An Application Security Architecture
  2. Network and Deployment Security Architecture and
  3. Data Privacy Considerations

Conclusion

A typical automation project comes with a promise of enormous productivity improvement, cost optimization and reduced errors / rework. However, not selecting the right tool with strong security features can not only deprive the enterprise of the benefits of automation, but also cause a nightmare of audit issues, data corruption, data loss and breach of privileges in applications that are automated. While there are many time-checks and controls that are essential during implementation, the battle of automation is half won if the right software is chosen.

Evolving from Customer Service to Customer Experience with Intelligent Automation

AI and automation are set to disrupt the way contact center industry operates through a human-led digital workforce that can transform customer service delivery. Advanced capabilities, powered by AI and automation, will make contact centers agile and intelligent, opening a window of opportunity for them to directly influence the top-line. New-age customers, who love personalized, omni-channel and self-service experiences, expect their interactions with agents to be simple, insightful and contextual.

Research shows customers who have got satisfactory and delightful resolution to their problems are likely to spend more on the brand as compared to customers who never had any problems or customers who did not get a satisfactory response1. A Mckinsey study shows that companies focused on providing a superior experience across customer journeys realized an increase of 10-15% in revenue and 20% in customer satisfaction2. Meanwhile, a Forrester study shows that increasing customer retention by 5 percent can increase profits by up to 95 percent3. Hence, enterprises have the opportunity to relook at contact center investments using new-age technologies that can drive additional revenue and profit.

Challenges Facing Contact Centers

While there are opportunities waiting in customer delight, today’s contact centers are facing challenges in achieving basic customer satisfaction. Customer engagements with modern contact centers are expected to offer an omni-channel experience, an antithesis of traditional single-channel contact centers. To enhance customer experience, reduce operational costs and be at the forefront of change, contact centers need to engineer efforts to increase their effectiveness. In a stark contrast, contact centers using legacy systems face major operational inefficiencies, such as endless waiting time, multiple transfers, and lack of real-time reporting. These factors leave little leeway for customer experience enhancement, and lead to customer churn instead.

Contact center agents using legacy systems face myriad challenges in the form of managing disparate systems for information, incomplete customer information, compliance and SLA adherence issues and lack of knowledge on promotions and offers. Impediments, such as these, create high learning needs and pressurize agents to improve productivity. The ripple effect of these factors manifests in the form of high employee attrition.

These inefficiencies can only be addressed by contact center automation, making it critical for industry leaders to capitalize on this change and create their niche in the market.

Reimagining Contact Centers with Intelligent Automation

Intelligent Automation (IA) augments contact center efficiency, delivering solutions across all customer touchpoints. IA offers an intuitive approach to data integration and system workflow that enriches and automates contact center effectiveness in the following ways:

  • Predictive Intelligence – The pre-emptive feature of predictive analysis anticipates call reason based on transaction and call logs history and behavioural patterns, making identifying customer easy.
  • Conversational Virtual Assistants – Virtual assistants use Voice Recognition Engine and NLP engine to analyze customer sentiment, thereby helping agents to empathise with upset and angry customers. They also effectively answer all FAQs, which otherwise become tedious for agents.
  • Unified Agent Desktop – A consolidated view of disparate systems offers agents access to service and call handling metrics, making customer engagement more efficient.
  • Actionable Business Insights – Collating customer information from disparate sources helps businesses generate actionable insights that streamline performance management.

The efficiency of automation and AI solutions, thus, is not limited to solving problems. Their focus is wider, covering the pervasive need of enhancing the quality of service by touching every pain point that contact centers face.

Why AssistEdge Engage

AssistEdge Engage brings the power of AI and automation to reimagine customer experience in contact centers. Customer satisfaction can be significantly enhanced by understanding and optimizing agent journeys and engagement. AssistEdge Engage empowers the agent with digital assistants that perform repetitive and mundane tasks and lets the agent focus on customer-centric actions. This future-ready IA system helps deliver personalized experiences, which in turn, helps businesses maintain customer centricity throughout the journey.

This intelligent solution also fortifies data, reduces IT complexities, creates a 360° customer view, lowers operational costs and helps in realizing ROI faster. These factors work in favour of increasing the brand value by raising customer stickiness and net prompter scores.

Caption: With AssistEdge Engage, operational productivity follows a rising trajectory

Focusing on customers, the sentiment analysis feature of AssistEdge Engage deciphers customer sentiment based on recent customer interactions and displays its analysis on the agent’s dashboard. Integrated with cognitive services, this amplifies customer engagement, empowering agents with the awareness of customer emotion.

The agile, out-of-the-box dashboard templates build rich and professional-looking dashboards using the comprehensive list of component repository to suit process needs. While improving turnaround time, this makes on-boarding new implementers easier. The Citrix hybrid support allows seamless operations in Citrix or VDI environment, including hybrid scenarios.

AssistEdge Engage is an over-the-top application that integrates seamlessly with existing legacy systems. Investments thus are not directed towards a complete system overhaul, which transpires into shorter break even time for companies.

A Brighter Future, An Efficient Ecosystem

To stay relevant in the future, contact centers have to think beyond the elemental routing and scheduling software. They have to reimagine their journeys with a holistic strategic vision, with complete understanding of the various elements and components that become part of this system.

AssistEdge is engineering world-class automation solutions that will empower enterprises to experience gratifying customer interactions. The strong IT architecture of AssistEdge Engage harnesses the power of AI and automation, to empower its digital workforce with capabilities that help them solve all operational challenges with unmatched efficiency.

Source links:

  1. http://customerthink.com/taking-your-customer-service-from-cost-center-to-profit-center/
  2. https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/best-of-both-worlds-customer-experience-for-more-revenues-and-lower-costs
  3. https://smallbiztrends.com/2014/09/increase-in-customer-retention-increases-profits.html

Broadening the scope of Process Automation with Attended Automation

As Robotic Process Automation (RPA) adoption is rising with every passing day; most enterprises which have already implemented RPA and have been using it for a while now, have come to realize a few bitter truths:

  1. Not all business processes can be automated end to end in a touchless way. Parts of the process still have human dependency and require human understanding to comprehend & complete them.
  2. Processes are run by software robots which generally run on remote server machines. In case exceptions and fallouts occur, they have to be handled by humans.
  3. Whenever an RPA process uses a service that provides cognitive capabilities; like an optical character recognition (OCR), the accuracy of output from these, though improving, still remains a challenge.

In short, a human is indispensable in the RPA journey!

These points also bring us face to face with the fact that deterministic (or rule based) automation, which RPA caters to, can only cover a certain distance and raises a few questions about the future of automation, which are worth pondering.

  1. What’s next?
  2. Have we reached the saturation point of automation using RPA?
  3. Are further automation projects going to pose a challenge without human involvement?

RPA integration with AI and Intelligent automation with built-in self-learning capabilities is the industry’s forward looking answer to all these questions. This is popularly called as RPA 2.01. As per analysts, “It is expected that the RPA market is going to reach about USD 8.75 billion by the year 2024”1. But the path isn’t straight forward. Accuracy of intelligent and cognitive services still pose the biggest challenge! And to counter that challenge, “attended automation” undoubtedly has a pivotal role to play in realizing this RPA 2.0 vision. And to answer the question pertaining human involvement, yes human does have a role to play and we will uncover the details in this blog.

Let us first understand attended automation, look at how it addresses the current roadblocks in RPA and broaden the scope of process automation. We will then see its role in realizing the Intelligent automation vision.

Defining Attended Automation

Attended Automation is the flavor of assisted RPA where the bot sits on the agent machine and is triggered by the agent for automating tasks in a big process. It saves agents’ precious time automating swivel chair2 operations and mundane tasks, enabling them to do value added tasks which require human judgement and intervention. Our whitepaper on “Demystifying Assisted Automation” takes a deep dive into the topic based on the usage and context (e.g. in Contact Center).

How does attended automation address the roadblocks in RPA and broaden the scope of process automation?

For processes that cannot be automated end to end using RPA, we recommend some quick checks and balances:

  1. Automate parts of a process only if the benefits in automating them are higher than the costs and maintenance overheads of automation .
  2. Processes for which it does not make business sense in implementing an unattended RPA, use attended automation for process orchestration under human vigilance. The human fills in where automation is not possible and completes the process seamlessly.

Attended automation helps leverage the benefit of automation along with human intelligence where end to end automation is not possible.

Handling exceptions and fallouts in the unattended RPA processes:

  1. For handling exceptions, integration between unattended and attended automation can be a boon.
  2. Output of RPA request execution and failure reasons is made available to the agent handling the exception. With attended automation, the agent has all the applications involved in the process available for reference.
  3. Looking at the state of applications and reason of failures human agent, tasked at handling the exception can take a guided call on fixing the exception and automating the rest of process.

For automated process which are error prone, it is a good idea to build them as Attended automation processes.

Role of Attended Automation in Intelligent automation journey

These days’ use cases that involve reading of scanned documents using OCR capabilities, are already in definite consideration set for automation. The use of the cognitive capabilities like image based automation, OCR, computer vision, AI, ML mark the start of intelligent automation.

A genuine challenge in the use of cognitive capabilities is the correctness or rather incorrectness of output from the cognitive services providing the capabilities.
In such a scenario, a “human in the loop3 is a key capability to fill in this accuracy gap, make corrections to output where required and proceed further with automation.

To take an example of Invoices processing in the Accounts Payable flow where the invoices are received from vendors over an email. These invoices are read and details are fed into SAP after which they are taken up for further verification and processing. Often invoices are scanned and OCR is used for reading data from the Invoices, which gives the data a certain accuracy. Here attended automation can help to orchestrate this automated process and present the data read from the invoices by OCR for human eyeballing and correction. The corrections are fed back to the OCR model to train the same. This improves the accuracy of the OCR model needing less manual intervention over time.

Another example from Insurance could be claims processing where machine learning (ML) is used for getting the claim classification and in case of low confidence of the ML algorithm, it needs to be routed through a human for making decisions. Again attended automation becomes handy to manage the exceptions and based on the choices made by the human, process the claims further. The decisions are also captured and fed to the ML model for training.

RPA industry is now closest to bridging the gap between pure play RPA and AI/ML capabilities which were usually considered as distant capabilities.

What a teacher is to his students, attended automation is to intelligent automation. It is set to be a perfect teacher with the tool and means, to train the students (OCR, ML, Vision and the likes) and build a bright future to take automation to newer heights, seemingly unimaginable today.

So, if your Enterprise is on the digital journey and has started with RPA; without wasting any time, make sure to add attended automation to your toolset so that you are ready to embark the Intelligent Automation aka RPA 2.0 journey and reap the benefits from the same.

References:

  1. www.roboticprocessautomation.co.in/the-future-of-robotic-process-automation-the-next-big-thing/
  2. https://www.webopedia.com/TERM/S/swivel_chair_interface.html
  3. https://hackernoon.com/what-is-human-in-the-loop-for-machine-learning-2c2152b6dfbb
  4. https://www.edgeverve.com/demystifying-assisted-automation/

Why Banks Should Get On The Intelligent Automation Wave

Banking involves a lot of manual activities which are repetitive and time consuming. The Banking systems have evolved from managing ledger management manually to Core Banking Systems (CBS). With the evolution of Indian economy and hordes of banking products and services available, there has been an exponential growth in the banking business.

It is no surprise that the banking industry trends tend to change but the lending trends rather stay consistent. When it comes to giving a loan, the request for a loan application goes through an entire complex process of approvals. A lot of steps in this process are rule based and some need to follow a specific trend and many of them are manual interventions.

In the recent past there have been a lot of examples where the borrower has not been able to repay, however there has been no responsibility taken by the sanctioning committee for the default. Analysis of the cash flows of the organization, future plans, repayment plans have to be put in place before the loan can been approved. In this process, credit ratings play a major role. Individuals with credit ratings above a certain value are considered as ‘low-risk’, who can pay their debts on time. While this is just one of the examples, there are various other rules that have to be applied before a loan is sanctioned.

Of late, you would have seen a tremendous increase in bankruptcies being declared by premium businesses. The bank’s limitation in recovering the money in case of liquidation of businesses. As per the latest examples given by the SMEs across banking industry, there is enough and more existing data that can throw some light on the trends in non-performing assets.

Getting to know the pattern of the financial ratio’s calculated from the cash flows varies from sector to sector but there is no trend that is followed by the banks currently.
There are lot of applications that need manual intervention for the amount to be approved but there has been no trend to the applications being approved, usually the application is approved based on a lot of other factors which go unaccounted. To stop this there has to be a trend analysis study of all applications and they have to run through the rigorous checks for the approval.

The biggest bottleneck in banking industry is the repetitive nature of activities being performed which have rules but end up taking lot of time because it still runs on lot of old school methods. Identifying the trends/processes and rules will not only save time but help in reducing the turn-around time and decrease losses.

Banks need to look at automation projects as an investment. This in-turn helps in quick results with less resources in the loan processing department. Considering the extensive change in the psychology of our economy we would need to get the approval processes automated with least intervention in the manual decisions so that the approvals are quick. This will also help in optimizing manpower efficiency. Getting a competitive edge over our competitors and quick processing can become one of the USP’s of the bank upon adopting automation.

This is the hour where banks need to transform digitally for achieving a competitive edge over other banks and gaining an exponential increase in revenue. With AssistEdge, a leading RPA product integrated with Artificial intelligence, EdgeVerve is best suited to meet these requirements of digital transformation. With its parent company as Infosys Ltd. the technology stack available is undebatable. So are you ready to start?

Have you assured intelligence for your enterprise automation program?

Evolution is a continuous process. And it is no different, when it comes to technology. As technology continues to evolve, companies have begun to understand its contribution towards increasing business efficiency and employee productivity. Automation has become an important factor to help businesses thrive in a highly-competitive market. Especially Robotic Process Automation (RPA), which has become the sure shot success formula for many first-time players in the sphere of automation, as a more viable and accessible option.

Having pervaded extensively into businesses in the past two decades, RPA has been successfully improving business performance and cost efficiency. And now, it is time to look beyond RPA. In this dynamic environment, automation also needs to go through certain evolution to continue to be fruitful to organizations. While automating rule-based processes is the first step, taking intelligent decisions is the next inevitable step in the automation journey.

Making Robotic Process Automation (RPA) Intelligent

To stay relevant in the market and be able to cater to the demands of the customers, enterprises are becoming increasingly aware of the need to align their strategy and investments to future-based technologies. To be able to do so, their automation requires cognitive abilities to comprehend the vast amount of structured and unstructured data, continue to learn, and intelligently automate processes.

What is Intelligent automation? An advanced version of RPA, essentially a software that mirrors the behavioral pattern of an end user to evaluate, calculate, transform and enter data into existing application fields as per the business rules can be termed as intelligent automation. It broadly constitutes of Machine Learning, Autonomics, Computer Vision and Natural Language Processing (NLP).

Machine learning is the ability of a system to automatically discover patterns in data and carry out predictions. This capability helps improve performance through systems that generate a lot of data over time. An everyday example of this would be Alexa, which is constantly learning from its environment and improving its responses.

Autonomics on the other hand, refers to systems, which perform routine tasks processed by humans. Today, it is becoming increasingly common in back-office operations performing high volume and rule-based tasks. It is predicted that autonomics will completely transform the Business Process Outsourcing (BPO) industry.

Computer vision is nothing but the ability of systems to identify objects, scenes, and activities as images. For instance, the face recognition software that are extensively used on mobile phones and social media platforms is based on this technology. Computer vision is widely used in security and fraud detection activities.

Natural Language Processing (NLP) helps interpret human language in the proper context to take appropriate actions. The most popular application of this technology would be Siri, available on iPhone and Mac devices. NLP is also being widely used for translation.

With these added layers of ML and other AI capabilities, intelligent automation functions differently from regular automation. For example, if you missed filling a section or entered wrong data in an electronic form, automated system would either reject the form or raise it for human intervention. Whereas, an intelligent system will identify and rectify the issue without human intervention. This self-learning ability and application of intelligence helps businesses function with much more efficiency and accuracy in terms of effort and duration, thereby enhancing the overall customer experience. Such advances in artificial intelligence, robotics and automation are becoming important for companies in all sectors to understand the impact and adopt intelligent automation or risk lagging behind.

How is Intelligent Process Automation driving improvements across value chain?

In today’s highly competitive and dynamic environment, businesses focus on customer experience. Intelligent process automation helps businesses in multiple ways to achieve their customer centric goals. Here are a few everyday examples how IA can unleash significant labor capacity while minimizing operational risk across the customer-facing facets through multiple capabilities.

Documentation: Most businesses have a lot of customer details. It gets difficult to segregate data as per the need and correspond with particular customers. Thanks to ML capabilities, an intelligent system helps understand the different requirements of customers, extract insights of the data and generate information accordingly.

Electronic mails: In an email, NLP helps comprehend the context and enables the system to carry out follow up action. ML helps collect data from past events and make faster and informed decisions. Also, the system is also enabled to draft ‘thank you’ mails to prospective customers after the concerned department has contacted them.

Raising Invoices: In case of delayed or forgotten payments, intelligent systems are enabled to send reminder mails to clients, customers, vendors and business partners.

Event invites: Invitations are sent to prospective attendees by tracking their location and analyzing their chances of attending the same.

Recruitment & Retention: An intelligent system makes recruiting easy for HR, by tracing incoming mails from candidates, reading and comprehending the same, scanning the resume and reverting to the right candidate. While the intelligent system does this, the HR can focus on higher-level tasks, which involve employee satisfaction and retention.

Summary

The main objective of Intelligent automation is to make machine more human-like. When organizations are automating non-deterministic tasks, they are able to make more informed decisions, without depending on language- or vision-based analysis, gaining a significant competitive edge. Intelligence in machines can extensively expand the scope of automation into newer areas, which are otherwise considered too complex.

While intelligent automation is still in its nascent stage, considered more like a theoretical concept, it is finally here, and will stay for good. The time has come to move from a mere deterministic and predictive automation to becoming more cognitive to assure inclusion of intelligence in your automation journey. With AI fast becoming the norm across businesses, leaders across business sectors would have to adapt AI sooner or later to remain competitive in the global market. So, is your enterprise ready to experience intelligent automation?

Why end to end Process Discovery is essential for de-risking Automation Investment

“If you can’t describe what you are doing as a process, you don’t know what you are doing” – W. Edwards Deming

Process leaders across the sectors are deploying innovative products to digitize, re-design and automate the processes till its last mile. This provides them a platform to rapidly drive cost reduction while also improving quality and timings.

To ensure the efficient last mile re-design and automation of process, an end to end view of the enterprise process is essential. Traditionally, process leaders have relied on manually mapping the processes either on BPM or as an SOP, which can be biased. Moving from user to user and department to department, more than 70% of the enterprise processes do not follow the standard operating procedure. Processes keep going through a certain pattern over years and years thus becoming an inherent and unchangeable part of the organization. Companies do not realize the inefficiencies of such processes until an external force shakes the structure to ensure processes are re-designed.

Without clear knowledge of process, enabling tech based redesigns and automation haphazardly can not only lead to but also cause operational chaos. Process leaders may be able to create pointed solutions to tackle bucketed problems but without end to end process visibility investment will have little or no impact at scale.

For example: Process leader may be hearing about lot of operational inefficiencies in Finance department and based on initial analysis with few users, will be able to map, re-design and run automation on let’s say Invoice processing. However, problem arises when there are lot of other users that process Invoice with some variations like using different screen route within application or treat different items differently for invoicing. This is where the anticipated efficiency and return on investment will fall off, making the Process leader to back to drawing table and re-assess the processes.

To ensure process leaders are moving towards the path of success in their tech enabled process re-design and automation initiatives they must:

  • Develop an end to end process playbook – This will enable them with clear view of the processes from highest (enterprise) to the lowest (user action) level. End to end process playbook will help process leaders to understand the process path and variations at a detailed level.
  • Capture and analyze comprehensive list of process metrics – Process metrics and analytics will help process leaders to take a crucial decision of which processes to automate vs re-design, what are the bottlenecks and which process do not have quality.

The next big question now is – While process leaders need to ensure that they are getting visibility to process playbook but can technology enable them?

Future of Process Re-design & Automation

Advancement in technology especially AI and Machine learning with improved data capture and human intelligence, has now created a new set of opportunities. Process leaders can now leverage AI based algorithm to give them a guided understanding of which processes to focus and which user tasks to automate. Enters Automated Process Discovery – mining the processes and discovering the tasks. Automated process discovery as per Gartner – is a complementary approach to create business process model at a fraction of time and cost. It fills the process knowledge gap that exists between departments and functions.

Automated Process Discovery can be divided into two key areas:

  • Process Mining
  • Task Discovery

Process Mining: It can use logs from various enterprise systems to automatically generate business process as they are happening in the enterprise. For e.g. Process mining can automatically map out for what items Finance department is matching PO to Invoice and for items it is processing invoice without any match. It can give you metrics like lead time, cycle time etc.

Task Discovery: It uses AI to automatically identify user level actions for each process. It monitors the key strokes, click stream actions performed by the user in his/her system to complete a task that is related to a process. It captures all the task variation and then automatically generates the task map. Task maps can enable the process leader to precisely look at automation candidates that can provide high business value as well as are technically feasibility. Task discovery also provides important data analytics – like time to complete, variation percentages, # of clicks, copy/paste etc. For e.g. Task discovery can automatically map multiple application, steps and variations users are taking to do PO to Invoice matching in their Finance department.

Both Process mining and Task Discovery can act individually, however the Process discovery journey of Process mining and Task Discovery working in tandem can become the holy grail for Process Leader. It can help them to continuously monitor the process patterns, user task variation, provide mission critical metrics and create a comprehensive playbook.

Revisiting the example, we discussed above: Process leaders upon hearing about a lot of operational inefficiencies in Finance department can plug process discovery to create a playbook of each and every process route, tasks within that process and key metrics thus capturing highest level to bits and bytes of information. The playbook will enable them to identify Invoice processing as the key area of focus with all its issues like -invoices are getting processed without an approval on critical items and that approximately 80% of users use 5 screen toggles and 1000s of clicks in application to create an invoice as compared to 20% using just 2 screen toggles and 100s of clicks.

This comprehensive playbook, playing real time processes and metrics, can act as a reference point for any re-design and automation opportunities in future. Process leaders thus need to take the charge and ensure such real time end to end process discovery in order to make their investments count with high impact at scale.

Steps to assure enterprise automation scalability

Imagine a scenario where an enterprise decides to automate a core business process with RPA. They choose a vendor, select a process and after a successful POC, go live and derive expected results out of it. Now the business grows rapidly, and the automated process or the RPA technology they adopted doesn’t scale as needed. What started as a boon, has now turned into a roadblock. Ever faced with such a scenario? This is what most enterprises face, when it comes to RPA.

While we have seen various enterprises adopt RPA to further streamline their operations and improve efficiency, they haven’t been able to scale it up to realise its full potential. What starts with promising POCs have often failed to deliver. Why?

Well! Many enterprises don’t foresee the need for scaling up and therefore, lack what it takes for a large-scale implementation. When the full-potential is not taken into consideration, it often leads to insufficient business cases being used to set-up the required architecture and infrastructure. As it is not planned for a larger good of the organization, all stakeholders aren’t required, leading to significant resistance from various departments within the organization hindering the success of implementation.

So, what does it take for a successful automation scaling?

A successful RPA implementation is the first step towards enterprise-wide digital transformation. However, the scalability of RPA plays a huge role in achieving long-term success with automation. Below are a few best practices from organizations that were able to scale their RPA efforts to realise its full potential.

STEPS TO ASSURE AUTOMATION SCALABILITY

Conclusion:

Implementation of RPA at scale is a task achieved by few enterprises till now. However, it isn’t unachievable. If enterprises define a clear purpose, involve key stakeholders, collaborate with IT, create a Centre of Excellence to adhere to best practices and engage with their implementation partner for a continuous feedback mechanism, they have a sure short at a successful and scalable RPA implementation. By aligning people with the processes and taking an inclusive approach towards digital transformation can set high standards and help businesses move faster towards their digital goal, resulting in scaling their growth and profitability. EdgeVerve’s AssistEdge, with its robust capabilities, has the power to scale your enterprise automation program and meet the defined goals.

The Importance of Data Security in RPA

Robotics Process Automation as a solution is being embraced by organizations around the globe. The implementation of such a solution involves bots that are responsible for carrying out crucial processes across multiple business functions. These virtual bots operate like a human and replicate repetitive tasks that a user would typically execute on a day to day basis. While they are deployed to improve efficiency and accuracy of a process; they need to have access privileges to a plethora of business applications in order to execute the tasks to near perfection; and with such responsibility comes huge accountability of data security and access security.

While most of the implementations serve a purpose of process efficiency, users often find themselves at risk of security threats. Think of how an organization empowers all employees with crucial deliverables, responsibilities and most importantly, information. Should one of the employees turn rogue and whimsically decide to divulge confidential information to the external world, that will result in a major cacophony across all stakeholders of the organization posing serious credibility to their data security framework at place. When a company implements an RPA solution the robots will be enabled to access databases, extract data, consume user credential and access applications. A corrupt robot can pose a serious security threat to an organization and all the stakeholders involved.

Broadly security threats associated with an RPA implementation can be classified into:

It is understandable that the security threats aforesaid can create a sense of apprehension to a prospective business user on the implementation of RPA for their business process. However, there are pre-emptive measures that the organization can partake to ensure such a security breach never occurs in the first place. As per a 2018 report by Ernst and Young for RPA implementations, organizations should consider the technical, process and human elements of the entire robotics ecosystem. A secure implementation should be in accordance with the entire product lifecycle starting from requirements, architecture to the ongoing operations.

The following four modules are the key pillars of Data Security while implementing an RPA solution for your business:

While we have touched upon the various security risks that comes with an RPA implementation and also means to overcome and continuously monitor risks; the business benefit on the other side of the tunnel makes the effort worthwhile. RPA itself as a solution can orchestrate the compliancy and security of the organization seamlessly. Organizations can use robotics to reduce response time to security threats and automatically deploy security controls when vulnerabilities are detected resulting in a reduced attack surface. How you may ask? An RPA implementation can limit access to applications/systems, application credentials and database. Further it can help keep a trail of all tasks/workflows, record changes and exceptions; all of this while keeping an accurate time-stamp of all activities.

There is no doubt that RPA is the way ahead as companies are implementing automation solutions increasingly to reduce costs and automate repetitive tasks. Security is an aspect that is increasingly coming into the considerations of CIOs and CISOs around the globe and RPA will play a major role in future to help organizations stay secure and compliant while seamlessly executing business tasks faster and cost-effectively.

References:

Automating and redesigning processes to amplify business impact

Automating tasks is not a new phenomenon. We have been slowly and steadily getting accustomed to automations over the last decade or so. Take accessing your music library on your smart phone for instance. So far, we would need to pick up the phone, unlock it, go into the music library, and select the album or song we wish to listen to. This in itself was a phenomenal breakthrough, having an entire music library on one device. But we needed faster and swifter access than the steps I just described. Hence, automation came to the rescue in the form of Alexa, Siri, Google Home etc. Now, all you need to do is say, “Hey Siri, please play Wish you were here from Pink Floyd”, and it does!

Automation is here to stay, and is evolving rapidly. Most of the robotic process automation tools today configure rules into software bots that run non-intrusively on heterogeneous systems. It is way easier to train these bots just by imitating the task user does across underlying applications. The expectations from such RPA implementations are to amplify the business value through significant productivity increase, reduced turnaround time, and improved quality of execution.

But, the underlying data from actual implementations show a different facet. As per a recent report from Ernst & Young, 30% to 50% of initial RPA projects fail1. A Deloitte study suggests that 63% of the enterprises did not meet the delivery deadlines for the RPA projects2. Only 3% of the enterprises responded that their automation program has met more than 70% of its objectives. So what has gone wrong in their automation programs? What challenges the businesses typically face while implementing RPA which is failing to give desired benefits?

To diagnose this better, we first need to understand how do businesses decide and prioritize the processes and subsequent tasks to automate. Currently, most of the enterprises are hiring consultants who interact with subject matter experts (SMEs) and operation agents for process knowledge to do automation evaluations manually. Following are the typical challenges faced by consultants during manual evaluations:

These factors make the automation ineffective and increase the risk to automation program. The success of any automation program strongly depends on the deep understanding of how processes are actually getting handled on-ground.

So, what’s the way forward to unleash the value and potential of automation?

The answer is automated process discovery. This approach gathers the actual on-ground execution data, analyzes it, and backed up with substantial empirical data it creates the automation blueprint. It also not only eliminates the human biases from automation evaluations, but also enables process SMEs to keep the SOPs updated with on-ground innovations or to do audits of the ways tasks are actually getting executed. Needing minimal interactions with stakeholders for process understanding, this accelerates the automation assessment with rapid discovery at large scale.

The automated process discovery would also elicit opportunities of re-designing the subsequent tasks to amplify the value derived from automation by providing visibility into end-to-end process execution based on actual on-ground data.

The automated process discovery is the need of the hour to enable Automation CoE to baseline the as-is process by collecting and analyzing empirical data, and assess continuous improvement and RoI. It accelerates and amplifies the true value of automation.

So, what are we waiting for?

How Intelligent Automation is revolutionizing the Banking Industry

Retail banks have been the pioneers in adopting Robotic Process Automation (RPA) for their business operations. As per a survey report by Everest, the adoption rate of RPA by retail banks has increased from 20% in 2012 to 50% in 20171. With such a fast moving platform being adopted across industry, no bank can afford to delay responding to the call for scale, which will significantly enhance the digital experience for their customers.

70% of banks are expected to invest in technology to strengthen their competitive positioning and build market share over the coming three years2. To increase their own customer base and have an advantage over others, banks are focusing on becoming a world-class digital player. No bank would want to lose a customer to another that offers faster processing and convenience.

There are many aspects that are changing in the digital banking world. Banks will not just compete with other banks but also other digital payment mediums or micro banks, such as Paytm, Googlepay, Mobikwik, Paypal etc. These mobile enabled platforms have made finance management as easy as swiping your phone screen to answer a call. The new entrants in the Fintech space who are now threatening the traditional banking business are Apple & Samsung with the launch of their mobile credit technology, which use biometrics, thus making them highly secure.

Automating this from the RPA perspective cannot happen today without involving the mobile devices in the RPA process. And mobile devices cannot yield results without a knowledge management system with artificial intelligence. Can you imagine a human workforce without their dependency on mobile devices? Wouldn’t it be a disconnected world if you are not able to leverage the most powerful source of information which we carry every day?

It matters to the bank as the mobile device is the digital face of the bank interacting with the customer. The customer behavior is well understood by the bank application which resides on the customer mobile device. At the same time, automating it would mean dealing with those many exceptions and a customer doesn’t forgive bad experiences at all.

To manage this change in the banking organization, the below technical process will be needed:

In doing so, the bank will face the following challenges:

With a market leading and innovative RPA product like AssistEdge, EdgeVerve will be able to fit amongst the existing RPA infrastructure. Since the technology infrastructure is managed by the same parent company – Infosys, there will be no outages or disruption in the existing way of RPA and customer experience. One such example is the industry leading Finacle banking suite which not just powers banks but runs critical banking infrastructure for many nations.
On the other side, AssistEdge is leading the Intelligent Automation market with its capabilities of Knowledge, AI, Vision, Machine Learning, Natural Language and Speech capabilities. These capabilities have smoothly integrated with the enterprise existing infrastructure in the past and AssistEdge has successfully enabled top 50 banks across the globe to scale up to the call of workforce automation and excellence.
As a bank, you will have to act on choosing the right product and service now, to be a market leader through digital banking and RPA. Start today in creating an enriching digital banking experience for your valuable customers.

References:

1 – https://www2.everestgrp.com/Files/previews/Everest%20Group%20-%20Banking%20BPO%20Annual%20Report%202018%20-%20Complimentary%20Abstract.pdf

2 – https://www.ey.com/Publication/vwLUAssets/ey-global-banking-outlook-2018/$File/ey-global-banking-outlook-2018.pdf