The impact of RPA is far-reaching in multiple sectors, especially in industries that are playing a critical role in fighting the current pandemic.

Health Sector:

RPA has immense impact across the value chain of the health care industry, be it: testing, diagnosis, monitoring or treatment. RPA programs can take the patient’s test records and document them in the hospital’s electronic medical records while sharing the record with the health department. Such processes without any manual interventions accelerate the testing and monitoring capabilities of the hospital but also the government to monitor the crisis. Hospitals can automate their order fulfillment during the surge in demand and health screening with bots measuring the temperature to attain operational efficiency.

As the healthcare sector is at the forefront of the fight against COVID-19, operational efficiency is crucial for hospitals. The healthcare staff are exposed to the patients and facing the highest risks of contamination, automating some of the repetitive processes will reduce the exposure and the risk.

Retail Sector:

With the major markets are currently under lockdown, the retail sales have been all-time low. As several countries have limited retail to essential goods, there has been a sudden surge in demand for these goods. While the retailers are struggling to manage their inventory, delivery services have also been affected by a reduced number of workers. These challenges are inspiring retailers to automate order handling and inventory management.

The brick and motor retail players are betting on self-checkout terminals, shelf-scanning robots while e-retailers are experimenting in using drones and robots for deliveries. These disruptive innovations in RPA may change the way the world sells goods, outlasting the current pandemic.

Financial Services:

RPA automates the intake and processing of documents submitted by customers, which are then automatically verified, along with KYC norms and approved/rejected automatically. Such document processing and verification functions in financial services are helping businesses to accelerate service delivery. In fact, the automation in financial services reduces the risk for the companies further, as these programs perform functions based on consumer’s historical data.

Travel and Hospitality:

COVID-19 has led to wide-scale flight cancellations, rescheduling, etc. Flight operators are leveraging RPA to help customers self-manage the same – including rebooking, refund, cancellation, personalized alternatives, etc. Airlines are using RPA to automatically assign flying crew for the restricted flights which are plying especially for domestic travel. While the threat of COVID-19 continues to affect demand, in order to create a safe flight in the future, Airports can deploy RPA programs. These programs can scan the travel history of travelers, find if they visited any infected places, and augment that by tracking individuals with fever. These programs can act as one of the security tools for safeguard passengers, creating confidence among travelers to fly in a post-pandemic future.

Manufacturing:

Among various sectors, the manufacturing sector has been proactively adopting RPA. With 85% of the RPA market still untapped, the current challenges are encouraged all businesses to dive into automation. From supply chain to assembly lines, the major manufacturing sector has been embracing this transformation for the past few years, while small and medium scale manufacturers are slowly shifting their key processes to RPA, starting from hiring and managing laborforce.

These are exceptional times, unprecedented times and the promise that RPA/Automation holds for companies across industries is undeniable. Today, several new use cases have emerged and as the situation unfolds, where it is expected to worsen before it gets better, RPA/Automation adoption will further intensify and more such disruptive use cases will come to the fore.

Business Challenges that Intelligent Process Automation can tackle

Based on our engagements with C level executives, we have identified the bellow challenges:

General Challenges

• High cost of operations
• Low processing time for completing certain tasks or generating reports
• A very high volume of transactions or paperwork such as invoices, requests, POs, contacts
• high error rate
• In ability to meet KPIS and regulations put by regulators such as Insurance, banking, health and telecom

Covid 19 Challenges

• Rapid need for recruitment in peak periods and after the lockdown is over
• Handling unexpected volume of work such as in:

Healthcare sector:  rapid increase in testing diagnosis and tracking cases

Airlines: ticket cancellation and refunding

• Business continuity for back office and front office tasks during lockdowns
• Supply chain disruption causing shortage in warehouses and Point of Sales

Benefits of Intelligent Process Automation

• Reduce process cycle times between 80% to 90%, enabling quicker time to market for new products and services, improved customer satisfaction…
• Around 4x increase in process capacity allowing firms to scale critical processes without increasing expenses like headcount.
• Allow Employees to focus on critical and valuable work by removing 80% of the non-valuable work.
• Achieve more with less resources
• Providing 24/7 service to reach more customers
• Reduce the error rates to almost 0 %
• Deliver between 25% to 50% in savings
• ROI is between 6- 9 months

Areas where Intelligent Process Automation can be applied

• Automated data Extraction and Validation including Arabic Language documents
• Automating IT operations and Legacy system integration
• Recruitment process, CV classification and screening
• Employee/customer/vendor on boarding
• Procure to pay
• Finance and accounting: bank reconciliation, invoice processing, order to cash, record to report
• Supply chain: inventory management, demand and supply, shipment tracking
• Insurance claim processing
• Banking services, fraud detection, account processing
• Automating airline booking cancelations and refunding
• Healthcare COVID-19 use cases: automating test results reporting, accelerating appointments and testing process

I will end this article with the following findings from a survey done by PWC with 871 CFOs from 24 countries or territories during the week of 20 April.

• As workplaces reopen, 46% of CFOs will accelerate automation and 21% are eyeing contact tracing
• Placing investments on hold, but not digital transformation

It is a new Journey which we are excited to take with our business partners and clients thought utilizing the technological innovations in the most efficient way to ensure survivability, business continuity and growth.

Technology has brought in significant game changers in the insurance industry that requires executives to implement intelligent automation in their businesses: –

• Customer satisfaction – For any business, the customer is the king, and the insurance industry is no separate from this fact. The sole revenue source of the insurance industry is the premiums being paid by the customers. Technology has changed how customers operate and transformed their expectations. The customer demands unique customer experience, 24-hour access, faster and hassle-free service, relevant product and pricing information tailored to their needs. Insurers need to innovate to buy customer loyalty. High customer satisfaction driven by the improved service and faster processing times that automation delivers is a driver of profit through increased customer retention.
• Insurtech – What fintech has done to the finance business, insurtech intends to do the same in the Insurance business. Stringent regulations, large balance sheets of the incumbents coupled with customers tendency to not switch the service providers made it hard for the new entrants. But, the flexibility in business model and inclusion of automation and machine learning has forced the venture capitalists to induce money in Insurtech. The exhibit 1 shows the investment and confidence put in by the venture capitalists in these new startups which surely is a risk on the market share for the traditional insurance service providers.

• Risk Prevention – Digital technologies that give rise ever-increasing amounts of data and ever penetrating insights make for more accurate pricing of risk, but they also help mitigate the risk and hence reduce the premiums. For Instance – self driving cars and newest safety measures in the automobiles reduces the number of accidents and hence the value of the insurance policies. Similarly, for home insurance, the fire sensors can provide early warning and hence reduce the damage and overall insurance policy value. This logically would mean that insurers would pay more for the devices that help risk prevention and less for the insurance policies, hence decreasing the revenue of the insurance company.

Use Cases for Intelligent Automation in the Insurance Industry

Underwriting

Underwriting is the most critical element in the insurance business. The assessment of risk and align it against a fee requires access to loads of data, data analysis coupled with human decisioning, for instance:

• Health risks – Mortality charges and hence premiums will be increase for the smokers especially when weighed against the applicant age
• Financial limits – If the net worth of the applicant is $X, their insurance coverage cannot exceed $10X
• Creditworthiness – What is the credit rating of the applicant as per the credit bureau agencies and then formulating the decision
• Duplicate policies – Is the applicant holding another policy in their name

Intelligent automation coupled with robotics process automation and machine learning can be of value in automating the underwriting process. The implementation of intelligent automation to automate underwriting can significantly reduce the processing time and assist in making the accurate decisions: –

• Data collection from external and internal sites
• Pre-population of data fields in internal systems
• Intelligent OCR to extract data from the scanned documents
• Assessment of loss runs
• Reviewing the customer history and claims and producing recommendations

• Policy Management

Another common use case for intelligent automation is the whole cycle of policy management operations, including policy issuance and updates.

Upon the underwriting decision, the policy must be issued, and the information needs to be updated in the internal systems and communicated to the customer. This activity is highly manual and prone to error as the dependency on the legacy systems is very high and nature of job is highly data entry. Intelligent automation can be used to automate the insurance policy issuance thus significantly reducing the amount of time and manual work required.

Existing policy holders can submit service requests for updates of the communication address or update of the bank mandate. Robotics process automation and machine learning can be utilized to extract policy updates either from voice transcripts, emails or other sources to make the relevant changes in the internal systems.

• Claims Processing

Fast and efficient claims processing is paramount to success for insurance companies — yet it is often a time-consuming, highly manual process that’s frustrating for both insurers and customers. Typically, claim processing takes several days as insurance agents must gather and check data from multiple sources, such as:

• medical certificates and reports (in case of life insurance or health insurance claims)
• photos of damaged baggage and flight boarding passes (in case of a travel loss claim)
• police reports, driver’s licenses and vehicle damage photographs (in case of an auto claim)

It may take even more time due to human errors, like mismatched financial data or customer details. Such delays may result in the loss of customers and other financial and reputational damage to the company.

Implementing an automated claims processing workflow, including claims intake, assessment, and finally, claims settlement, eliminates friction and cost by combining robotics process automation, machine learning, and human expertise to streamline and speed up claims-related operations.

Benefits of Intelligent Automation in the Insurance Industry

RPA coupled with machine learning can bring massive benefits to any insurance company. The major benefits being the return on investments, enriched customer experience and much happier employees (as they do not have to go through the ordeal of repetitive work)

Unstructured Data Processing

Every day, insurers must deal with massive volumes of data in various paper and electronic formats. To process a claim, an agent must gather data from multiple sources and enter it into a database. The process is manual and time-consuming. Repetition of the same routine tasks repeatedly hinders human concentration, resulting in errors and creating serious inconsistencies in company records. Robots, on the other hand, excel in this aspect.

Machine learning with robotics process automation can process a wider variety of documents more precisely, and can automate insurance processes end-to-end:

• Download, classify, compile data from external sources
• Read, sort, analyze and route emails
• Extract and analyze similar data from different sources (email attachments, transcripts, scanned agreements, etc.)

• Integration with Legacy Applications

Insurance companies still rely heavily on legacy apps and various programs and systems for managing business operations. Implementation of new software, such as BPM or ERP systems, often requires replacement of the existing hardware and employee retraining — which are significant investments of time and money. Due to these difficulties, many insurance companies are forced to stick to the old systems, although they no longer provide the support required for company development.

Automating operations in legacy applications are great robotics process automation use cases in insurance. Robotics process automation bots can use existing user interfaces, which means there is minimal or no need to change current legacy systems. Robotics process automation bots can imitate human clicks and keystrokes, which makes them easy to implement in addition to the existing software and hardware. Robotics process automation bots create links between legacy and new systems without coding. They switch between various systems and applications and conduct claims processing, underwriting, customer service, onboarding, and other operations — all at the same time.

• Additional benefits

Successful implementation of an intelligent automation program can be incredibly beneficial for an insurance company, potentially allowing an enterprise to:

• Reduce the operational costs and increasing the efficiencies
• Improvisation in the accuracy
• Faster execution of transactions
• Increasing overall business productivity and profitability
• Increase the regulatory compliance to 100%

Conclusion

The true power of machine learning and automation to change the insurance industry is just starting to be felt. The global analysis forecasts that down the road, these technologies will empower insurers to identify, assess, and underwrite emerging risks and identify new revenue sources automatically, with little human interference required, making insurance a potentially semi-automated industry.

However, the journey begins with a pilot model: develop a proof of concept, test the derived benefits, and extend deployments once successful

RPA has the ability to increase process speed and capacity, a useful addition to businesses at a time when productivity is taking a hit. By utilizing RPA platforms, automation can support teams in process management across multiple locations. Companies are able to develop and deploy a new robotics workforce that ensures processes are handled without delay. The automation supports departments, monitors and keeps operations in the backend running smoothly maintaining business critical processes long after we are out of this exceptional situation.

Agility as a Culture:

Business leaders are responsible for strategizing and creating a line of businesses (LoBs) that include the core business and internal functions. Ensuring uniformity in culture, policies, and tools across different LoBs, be it: data warehousing, developers team or the IT department is crucial for successful implementation. While organizations often cautious about uniformity in tools and policies, organization culture is often overlooked. This results in a negative business impact and can fail the entire DevOps project. In today’s fast-changing ecosystem leaders must act as change drivers, creating an organizational culture that promotes innovation and agility to support dynamic IT infrastructure.

This transformation in DevOps requires training, mentorship, behavioral change, assessment across all levels of the organization. Cultural and change management experts should approach DevOps with a concrete roadmap of implementation, milestones, and feedback loops.

While business leaders are involved in governance and strategizing; on the application level, there are several best practices that organizations must adopt for effective implementation.

Organize Application Cluster

Identifying dependencies among related applications and grouping them in clusters based on release time and strategy is key for successful implementation. This allows for the harmonious implementation of DevOps to all applications within a cluster, thereby promoting consistent release speed and quality.

Pilot Testing

Organizations can make a small investment to pilot a few selected applications to test the initial DevOps improvements and benefits to others. To identify these pilot applications, companies use different analytics techniques such as cost-benefit analysis, application characteristics analysis. If the outcome of testing meets the goal, the companies can then continue with other applications and further layers of adopting.

Continuous Integration and Delivery:

At the heart of DevOps adaptation are the continuous integration, delivery of applications and different improvements. Organizations use numerous tools and techniques to ensure successful DevOps implementation. Be it: application lifecycle management, automated analysis, or the deployment tools, these help to ensure an uninterrupted DevOps process throughout its lifecycle.

While DevOps success depends largely on the underlying cultural shift, tools are still important. Cloud applications and a combination of cognitive technologies such as Artificial intelligence and machine learning are helping in automating – testing, monitoring, and deployments processes, playing a key role in transforming a DevOps implementation project.

Leveraging APIs with AI and Machine Learning Technology

As the economies are becoming dynamic, the risk of non-payment and investments are highly threatening the finance sector. Financial firms are building AI programs that can predict the risk of an investment based on historic data. These historical data can be driven from an API for live monitoring and can predict the loss live before it occurs.

Regulating Security Protocols to Build Trust

In the financial service industry, trust is associated with how robust the security protocols and policies are. Apart from cutting edge technology, a data partnership requires companies’ security integration and its policies. Robust authentication systems and clearly defined access levels are crucial for building trust between businesses.

Creating New Revenue Streams

The traditional businesses of many banks have been facing slow growth, which has pushed banks to identify new revenue streams. In this attempt, banks are partnering with insurance companies, genetic and are building new products to make the customer banking experience better. The API is facilitating these innovations by creating a secure data interface for their partner’s app, web platform or software. Besides, banks are combining artificial intelligence and machine learning with APIs to identify investment risks or risk of defaulting based on the customer’s data or their profile.

Empowered Customers

Today, customers have become vastly more tech-savvy and technology has become part of their daily life. The new-age customers are open to new companies that offer better products, services, and experience. As customers are moving towards traditional banks to modern fintech, the competition is empowering consumers and inspiring traditional companies to change their approach.

New Competitors

The traditional banks that offer insurance services or the stand-alone insurance companies are fighting competition with new-age data-driven companies.

Hugo a web platform that issues instant, personalized insurance is one of the great examples of this. Based on the health risk profile of customers, Hugo delivers end-to-end digital insurance experience.

The financial services industry is seen disruption in the traditional approach and in technology, which is driving the finance sector to a new age. This has attracted new competitors such as fintech firms, challenger banks and neo-banks that use technology to challenge traditional business models and practices. Thriving in an API economy is key to unlocking new sources of business value for the financial service industry across the globe.

To solve  these challenges, developers need to design deep learning-based language models. These language models are able to analyze the sequence of the phrases and differentiate between similar phrases to understand the user’s most probable intent. Training the model with users from diverse languages, slang, or a high  level of communication skills makes the model much more accurate.

As businesses are transforming into data first enterprises, having access to consumer insights are becoming vital element to drive business strategies. Data solutions with NLP capabilities are helping enterprises to decode consumer opinion expressed on various social platforms and to deliver insights and predict attrition. Whether it is translating human language to machine language or translating between different human languages, NLP is critical to bridge the language gap between humans and the gap between humans and machines.

Organizations that are implementing device solutions are reporting that using natural language processing lets them deliver better experiences to  their language diverse customers in their own native language as well as the option to include other languages that are relevant to the business. Chatbots, applications or a website UI that lets consumers interact in their local language is becoming essential to reach markets that are untapped because of the language barrier.

Proven Consult delivers solutions including consumer insights, chatbots and integrate voice-based UI. To learn more about how we can help you contact us on info@provenconsult.com or +966 11 411 1127.

However, the organization’s objective behind implementing RPA decides the effectiveness of the solution. When companies apply RPA solutions, the focus should be on the question of what activities should be automated, rather than what professional role should be automated. It is one of the misdirections that most enterprises take, where business leaders aim to replace a professional role with robotic automation. Learning to see beyond a professional role and reframing the question to find a solution that meets the organization’s needs is crucial for successful RPA implementation.

With cognitive technology such as natural language, speech recognition, machine learning and artificial intelligence, RPA solutions can perform complex activities. These technologies leverage the RPA solutions with enhanced capabilities and transform the existing robotic automation into intelligent automation.

Where the RPA solutions can perform repetitive functions, these modern technologies enable applications to perform processes that are dynamic in nature. With intelligent automation, the application can decode the intent of a customer’s message and send an automated response or direct it towards an appropriate team, creating a seamless customer experience.

Although some of the processes may still have to fall back on humans as they require decision-making and human skills, the use-cases of intelligent automation are proliferating. These discoveries of new use cases are building the future of automation by bridging the gap between dynamic and repetitive processes.

Proven Consult delivers cutting-edge Machine Learning and AI-enabled RPA solutions designed to maximize business efficiency. We engineer solutions of the future to transform the enterprises of today with our expert and talented team of engineers and developers.

To find out how we can help you, contact us on info@provenconsult.com or +966 11 411 1127.