Bits & Bytes Vol. 2

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Message from the General Manager - Dr. Magd Donia

Ramadan Kareem! On behalf of KASP, I would like to wish you all a happy and blessed month. Once again, we go into Ramadan with COVID-19 restrictions, albeit with light at the end of the tunnel. Vaccination campaigns are underway, and promising signs point to an ease of restrictions by June. At KASP, we continue to service our customers just as we have done over the past 36 years. In fact, the headcount at KASP has now exceeded 40, providing services ranging from Business Intelligence and Analytics using Machine Learning, to ERP implementation and support, to Rapid Application Development using the world-leading low-code platform.

Complementing our long-established leading position in BI/Analytics in Kuwait, our leadership in ERP implementation and support has been recognized by IDC, which recently hosted a webinar featuring a panel of technology innovators in the Middle East, of which KASP was represented as one of panelists. Our innovative approach to ERP implementation, support, and BI, coupled with our recent deployment of sophisticated supply and demand planning, was especially highlighted during the webinar, which was attended by more than 150 professionals.

Dr. Magd Donia
General Manager, KASP
I’m also pleased to announce that during Q1, 2021, KASP has acquired four new customers in ERP implementation, ERP support, and low-code. I hope to share these success stories with you in our next edition of this newsletter.

We believe the outlook for the remainder of the year will be even more positive as we continue to deploy our hard-earned skills and expertise at our customers across banking, airlines, public transportation, media and government in Kuwait. Stay safe and God bless you all.

Robotic Process Automation! How it will impact theInsurance Industry?

The insurance industry was one of the first to witness widespread adoption of robotic process automation (RPA). The industry’s repetitive, rules-based, and document-heavy business processes make it an ideal candidate for automation. Many companies are now experiencing tangible benefits from RPA and the technology has a lot more to offer.

Anyone involved in any of the key insurance processes — underwriting, claims registration and processing, policy issuance and renewals, or, even, month-end reporting—know just how many pieces of information from many different sources are involved. It’s an industry that relies on its back-office processes and those processes are increasingly inundated, slow, and inefficient. For many companies, the challenge isn’t acquiring new customers, it’s servicing them properly. Contact centres are at full capacity and struggling to meet demand.

RPA is also lower cost and lower risk, and offers insurance companies a non-invasive way to automate core processes. For an industry that is burdened with repetitive and mundane tasks, automation provides a stable solution to increase organisational efficiency, customer satisfaction, and profitability. The productive impact of robotic process automation in insurance manifests itself with multiple facets. We introduce and briefly discuss five crucial ones.

Mr. Pathik Trivedi
Founder and co-CEO, Hybrid Workforce

1. Streamlined Data Processes

Insurance firms need to handle large amounts of data. If human employees are the only available option for data processing, this may take a lot of time and just as much money. Moreover, it leaves space for error which may increase even more the amount of time and money expenditures. But automation is an alternative that can do away with such disadvantages.

RPA makes data processes run more smoothly, let alone faster and error proof. The end result is a much more efficient data processing. This also has a corollary which should not be neglected, namely less bored, and thus more productive employees working on nonrepetitive, skilful tasks.

2. Integration of various systems and software

Innovation in the realm of insurance comes with a price, which may be quite costly. Yet no one can deny the necessity of IT improvement and technological development at all levels. Robotic process automation seems like the right solution to handle innovation properly.

It is ‘a proven technology that brings tangible benefits to the companies that deploy it and provides a means for companies to remain cost competitive in their market sphere’. RPA can easily handle the challenges of system updates and makes integration of the new much easier because it is consistent with the so-called “legacy systems”. As a result, it may lower the costs as well as the human expenditures involved in system and software updates.

3. Increased Compliance

Compliance with privacy regulations, and, relatedly, handling audits - both internal and external - are top requirements for insurance firms. The need to stay updated with the ever-changing laws that prescribe how privacy should be protected, increases the importance of the problem.

Robotic process automation can help a whole lot in this area as well. Because the software generates detailed logs of all transactions, it becomes much easier to track the processes and ensure that compliance with the regulations is in place. External audits thus become much less of an issue for insurance companies that make effective use of automation.

4. Increased Compliance

Robotic process automation may be seen as setting an insurance firm on an ascending path. Since software robots never get tired or bored, automated processes may be used 24/7, 365 days a year without fear of error interference. This means that RPA increases a company’s work capacity without any additional costs, neither financial nor with respect to quality.

Should new projects require additional skills and increased execution capacity, more robots can simply be “allowed into the field” without the hassle of training and re-training them every time. The equation seems to be very simple: robotic process automation in insurance equals more and better work for constant expenditure. And the best part comes when we acknowledge that this translates into maximally realising a firm’s potential for long-term growth.

5. Customer Experience

The impact of robotic process automation on insurance customer experience is twofold. On the one hand, RPA is very likely to significantly improve the customer experience by expediting and simplifying the claim process and thus making the customer more satisfied with the services.

Deloitte nicely points out this double-sided benefit of RPA in insurance: “Leveraging the plethora of capabilities offered by such tools, insurers can now design customer journeys from scratch rather than simply replicating existing journeys that are at best yesterday's stories with merely a bit more processing efficiency”. Not only does this improve customers’ experience with the insurance firm (which has a great potential for long-term growth), but it is also likely to bring about happier and more fulfilled human staff.

RPA use cases for the Insurance Industry

  • No Claim Bonus Management 
  • New Policy Issuance 
  • General Ledger Reconciliation 
  • Payment Reconciliation 
  • Invoice Creation 
  • Insurance Receipt Updation 
  • Tax Compliance Regulatory compliance data collection 
  • NTU Automation 
  • Group Policy Issuance 
  • Endorsement Processing 
  • Reconciliation Process 
  • Premium Accounting
  • Tariff Minus Rates Upload 
  • Instalment Tracking 
  • Risk score calculation 
  • Contract & Claim Process
  • Underwriting
  • Member Data Upload
  • Accrual Process
  • Claims Data Processing
  • Claims Adjudication
  • Claims first notice of Loss
  • Due Premium chaser
  • Bulk Payments
  • FMLA Leave management
  • Deceased Notification
  • Premium calculation
  • Claims support
  • Bulk Recoveries
  • Credit card payment reconciliation
  • Automate identity documents processing
  • Customer Onboarding
  • Life Insurance claims validation
  • Document Verification
  • Contract Review
  • Disputed Claims
  • Trade Deal Underwriting
  • Data collection for AML checks
  • Third Party verification


A 3x3x3 approach for Intentional Learning

Most of us have a desire to learn something new but turning that desire into new capabilities, requires a plan. It is essential to cultivate both the right mindset and the right skills to keep learning throughout our personal and professional lives. Too often, the goals that are set become goals unmet. According to a study by a famous Management Consulting company (McKinsey & Company), the best way to set learning goals and also succeed is using the 3x3x3 approach. According to the research we have to set 3 goals, have 3 month deadline & try to pick 3 people to learn with.

3 goals – “athletes in training shouldn’t work on too many muscles at once, neither should learners.”

When people set too many goals, they often fail to make real progress on any one of them. In fact, they often find it hard to remember what they’re trying to achieve. Having fewer concrete goals allows you to develop new habits and bring the right level of intentionality to improving your performance.

Mr. Mohammed Fazahir
SAP BI Consultant, KASP

3 Months – “Setting a deadline of three months also forces us to break down longer-term goals into achievable chunks.”

Three months provides enough “runway” to make tangible progress against a goal through cycles of practice, feedback, and (where needed) formal training and also a three-month period aligns with many of the natural rhythms of the organizational world, whether they be quarterly reporting, quarterly business reviews, or quarterly leadership updates.

3 people – “Socializing a goal also creates opportunities to celebrate and reinforce growth with others.”

There is a natural instinct to keep our goals to ourselves. It protects us from embarrassment if we dont achieve those goals and enables us to feel less vulnerable. It can feel uncomfortable to reach out for help but involving others in our learning is one of the most powerful ways to improve goal attainment. It creates healthy social pressure. It allows others to know where their feedback or ideas would be most useful.

That’s why people often find it easier to lose weight or exercise more regularly when they’re part of a support network as opposed to trying to change habits on their own. They share the challenge and the responsibility to stay on task. When teams make it the norm for each person to share individual development goals, the result is often a rich ecosystem for learning and growth where all members help one another. The most important aspect of 3x3x3 is not the precise number of goals, months, or people, but the idea of having a simple, consistent process for setting and achieving goals that we can replicate throughout our career.


Predictive Analytics – A Strategy for Driving Revenue Growth


Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, particularly in the areas of big data and machine learning. Do you know why? Today’s business organizations are working in an environment where there are threats from everywhere be it competitors, uncertain market conditions and so on. This is where they rely on predictive models for exploiting the patterns in transactional and historical data for forecasting the future with a certain degree of accuracy.

Enterprise Data

Enterprise data is a priceless strategic asset because it represents the aggregate experience of an organization, the very history of its interactions with customers. Each customer response purchase decision, acquisition, outright defection, act of fraud, credit default, and complaint of a faulty product component provides the nterprise experience from which to learn.

Mr. Justine Amalorpavaraj
Sr. BI Consultant, KASP

What is predictive Analytics

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modelling, data mining techniques and machine learning. Predictive analytics tools give users deep, real-time insights into an almost endless array of business activities. Tools can be used to predict various types of behaviour and patterns, such as how to allocate resources at particular times, when to replenish stock or the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.

Insights to Action

This is a current time for Business Owners and Decision Makers to upgrade from their traditional way of reporting analytics to data driven decision making which is insights to Action.

Business impacts of Predictive Analytics.

Here are some of the areas where machine learning and predictive analytics will prove to have major impacts:

  • Budgeting: Companies can use predictive analytics to forecast more accurately their budgeting needs, instead of having to speculate and rely on the old models of "what-if". As a result, collaborations between departments can be improved for better team-works.
  • Customers’ insight: as mentioned above, advanced analytics can help businesses produce actionable customer insights predicting future actions by consumers. Companies can use this information to create better products/services tailored specifically to their customers. Similarly, companies can also apply this principle to increase the conversion rates as well as improve customers’ loyalty, reward program, and more.
  • Cost reduction: With the customer lifecycle becoming shorter and getting more complex, adopting predictive analytics and machine learning technology will help companies to have more effective marketing campaigns, resulting in the reduction of expenses while generating more revenue.
  • Demand planning: Proper use of data analytics including predictive analytics increases the accuracy of demand forecasting by incorporating a wide range of external factors that influence customer buying decisions and preferences. These external factors can range from weather changes to economic expansion.
  • Gaining perspective: Business organizations can implement predictive analytics to gain insights into the future success of their new products and/or services. This is especially helpful when there is insufficient historical data available to make a forecast or when the past is not indicative of the future. Predictive analytics assist companies in making informed decisions in the absence of past experience.
Industrial use of Predictive Analytics

  • Aerospace: The amount of data that is generated in the modern era aircrafts is phenomenal. Today due to the abundance of sensors, newer ways of storing the data and finding various ways in which that data can be useful, Predictive Analytics is suddenly taking a huge stride in the aerospace industry.
  • Automotive: Today’s automobiles are heavily invested when it comes to deploying the most cutting-edge gadgets, technologies, sensors for coming up with highly valuable analytical methods for ensuring the driving experience is simply phenomenal. In the not so distant future, most of the automobiles will be connected to the internet of things and due to this the role of Predictive Analytics will only grow stronger.
  • Energy & Utilities: This is another domain wherein the role of Predictive Analytics is again very significant. It helps to predict the demand and supply of electrical energy through the power grids. There are various sophisticated models that are used for monitoring the plant availability, impact of changing weather pattern, learning from historical trends, forecasting the optimal demand and supply balance among other things that can help the energy domain save huge amounts of money and resources.
  • Banking and Financial Services: This is one of the biggest domains that is currently deploying Predictive Analytics at scale. Due to the large amounts of data being generated and the extremely high stakes involved, banking and financial institutions are increasingly deploying Predictive Analytics for ensuring the customers get a world-class experience that is customer-friendly, secure and forward-looking. It is possible to tailor-make products and services depending on the profile built around the customer, opportunities for cross-selling and up-selling, find patterns of fraud and malpractices among a host of other things.
  • Retail: The retail industry is working with predictive analytical tools and technologies to get inside the mind of the customers. It includes the process of stocking the right products, promoting the right products to the right customers, providing the most optimal discounts to persuade sales, having the right strategy for marketing and advertising among a whole host of other aspects.
  • Oil & Gas: The industry of oil and gas is a big user of the domain of Predictive Analytics. It helps to save millions of dollars through better predicting equipment failure, need for future resources, ensuring safety and reliability measures are met, and so on. There are a lot of sensor data that needs to be monitored in order to take the right data-driven decision in the oil and gas industry.
  • Governments: Since the data in a government department is humungous thanks to the allencompassing nature of this domain, there is a huge untapped opportunity which can be aptly exploited using the right Predictive Analytics tools and technologies. It could be deployed for providing the right services to the citizens, monitoring the various welfare schemes are reaching the right audience, checking corruption and malpractices and so on.
  • Manufacturing: Even in today’s world of servicesoriented economy the domain of manufacturing is still extremely important. The manufacturing industry can make use of Predictive Analytics in order to streamline the various processes, improve the quality of service, supply chain management, optimizing distribution and such other tasks for enhancing the overall business revenue and achieve bigger goals.
  • SAP in Predictive Analytics: Based on my research and analysis, I would recommend SAP HANA Predictive Analytics Libraries and SAP Analytics Cloud are the best tools which will give a better solution to all the industries looking to come out from their traditional Descriptive and Diagnostic reporting model to Predictive and Prescriptive Analytics model.
Below is the architecture of SAP HANA Predictive Application Libraries (PAL) which helps for forecasting business models. This application function library (AFL) defines functions that can be called from within SAP HANA SQLScript procedures to perform analytic algorithms.


Predictive Analytics provide immense benefits and help companies generate more accurate forecasts for business outcomes. SAP helps business to achieve their forecasting and data driven business model by their products like SAP HANA Predictive Application Libraries and SAP Analytics cloud.

Staying Late after Working Hours

Remember back when staying late at work demonstrated to everyone that you were a “real gogetter”? Staying late at work neither shows dedication nor increases productivity. In fact, all it does it perpetuate an unhealthy culture of overwork, not to mention make your colleagues resent you.

Why you should never stay late at work?

I know what you’re thinking. “Never stay late, are you serious? How is that even possible? I have to stay late to finish my work!” Yes, I’m serious. If your employer absolutely requires you to stay late in order to finish up a special project, that’s one thing. But if it’s a voluntary decision geared towards improving your reputation or demonstrating your undying commitment to the company, you may want to rethink that assumption.

The truth is, more often than not, your boss isn’t noticing the “extra work” you’re putting in. And if they do notice, they aren’t seeing the correlation between more time spent in the office and higher productivity. Spoiler alert: there is no correlation.

Mr. Arun M
Senior Account Manager, KASP

There’s a growing body of research that shows why spending more time at work doesn’t have the positive effects we might think it does. In fact, it has negative effects: it’s bad for your health, your productivity, and your relationship with your coworkers.

  1. Overwork is bad for your physical and mental health: This level of overwork inevitably takes its toll on our physical and mental health. Often, the physical signs are immediately apparent: chronic fatigue, lack of energy, insomnia, impaired concentration, and loss of appetite. In extreme circumstances, overwork can even lead to premature death. Mental health effects of overwork can include anxiety, anger, depression.
  2. Overwork actually decreases productivity: As far as productivity is concerned, that number isn’t far off. A 2014 study found that employee productivity falls sharply after a 50-hour work-week, and falls off a cliff after 55 hours—so much so that someone who puts in 70 hours produces nothing more with those extra 15 hours.This means that those hours you spend alone in the office after everyone else has gone home to their families don’t necessarily add up to more work produced. In fact, it can have the exact opposite effect over time.
  3. Staying late can create animosity between coworkers: Even if you’re perfectly fine leaving as usual, there’s still that flash of anxiety, the feeling that you’re going to look bad for doing so. It can create resentment among colleagues and negatively affect a team’s ability to collaborate. That said, a good manager will notice this and either put an end to it or reassure their team that they can leave whenever they’re done their day’s work.


On top of all this, regularly staying late at work reinforces a standard of overwork. It props up a toxic culture in which sacrificing family commitments and neglecting life outside of work (including taking care of our health) are the norm. This creates undue stress on people, and forces people to submit to the idea that their life is their work. And that’s not healthy for us or our society.


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