The article reflects upon the importance of data analytics in insurance/takaful industry especially for motor/car and medical business. Effective use of analytical tools can help in improving profitability of the motor business. Alongside, that can enhance customer experience in addition to cross/up sale opportunities.
Developing a Preventative and Sustainable P-card ProgramCaseWare IDEA
Andrew Simpson from CaseWare Analytics talks about how educational institutions can implement a continuous monitoring program for their p-cards (purchase card) and the benefits.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Keys to extract value from the data analytics life cycleGrant Thornton LLP
Regulatory mandates driving transparency and financial objectives requiring accurate understanding of customer needs have heightened the importance of data analytics to unprecedented levels making it a critical element of doing business.
Forrester Report: The Power Of Real-Time InsightSAP Concur
In a survey of 348 financial decision-makers around the world, Forrester found that T&E is the second most difficult item for companies to control. Most firms wait for their employees to manually enter their T&E data after the expenditure is already made so that the resulting T&E reporting process focuses on retrospective compliance and budgeting. Analysis of T&E trends and potential cost optimization, if done at all, is done primarily via spreadsheets.
Developing a Preventative and Sustainable P-card ProgramCaseWare IDEA
Andrew Simpson from CaseWare Analytics talks about how educational institutions can implement a continuous monitoring program for their p-cards (purchase card) and the benefits.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Keys to extract value from the data analytics life cycleGrant Thornton LLP
Regulatory mandates driving transparency and financial objectives requiring accurate understanding of customer needs have heightened the importance of data analytics to unprecedented levels making it a critical element of doing business.
Forrester Report: The Power Of Real-Time InsightSAP Concur
In a survey of 348 financial decision-makers around the world, Forrester found that T&E is the second most difficult item for companies to control. Most firms wait for their employees to manually enter their T&E data after the expenditure is already made so that the resulting T&E reporting process focuses on retrospective compliance and budgeting. Analysis of T&E trends and potential cost optimization, if done at all, is done primarily via spreadsheets.
When it comes to scrutinizing costs, most insurance companies can say “Been there, done that. Got the t-shirt.” Managers are familiar with the refrain from above to trim here and cut there. The typical result is flirtation with the latest management trends like lean, outsourcing and offshoring, and others. However, the results tend to be the same. Budgets reflect last year’s spend plus or minus a couple of percent in the same places.
Importance of High Availability for B2B e-CommerceSteve Keifer
This white paper explains how B2B e-Commerce technologies have become so critical to manufacturing and retail companies that further investment is required in high availability architectures.
Capitalizing on analytics in finance: Creating trusted insights for the enter...Spencer Lin
A recent IBM study of 337 CFOs and senior finance executives found that about 90% are implementing analytics solutions. However, analytics adoption is happening in pockets and isn’t pervasive across finance activities. With investment in analytics poised to double in the near term, how can Finance better capitalize on these new capabilities? To answer that question, we looked at the most effective Finance organizations and learned three important lessons from their success.
Automating Payables for the SME Market: Diving Head First into AP AutomationAnybill
This Technology Insight report is for small and mid-sized enterprises with an interest in payables automation. The report includes the latest adoption statistics, current thinking, best practices, strategies, and key performance indicators for evaluating and selecting the solution that meets your needs.
Imagine … internal auditors identifying risks + opportunities from data. Internal auditors can + need to grasp this opportunity to transform their role and industry. More >> grantthornton.com/data-analytics
Discussion of strategies for increasing profits without focusing on expense reduction but instead on areas with leverage like claims. Specific examples for gaining an edge are discussed.
Breaking the data barrier: Lessons from analytically advanced Finance organiz...Spencer Lin
The majority of enterprises recognize that data and analytics are transforming their businesses. But what about the Chief Financial Officer (CFO) and Finance? How are data and analytics changing their professions? To find out, the IBM Center for Applied Insights asked over 1,000 organizations across five industries about their Finance departments’ approach to data and analytics, cloud and engagement. Findings show that enterprises with analytically advanced Finance organizations reported better outcomes when it came to decision making, growth and agility.
Enterprises face unprecedented challenges, and finance is at the epicenter. Increasing business risk and volatility are evidenced by accelerating business disruption through
disintermediation, virtualization and technical innovation. As a result, new competitors, changing business models and changing customer expectations have emerged.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
The banking industry is data-demanding with acknowledged ATM and credit processing data. As banks face increasing pressure to stay successful, understanding customer needs and preferences becomes a critical success factor. Along with Data mining and advanced analytics techniques, banks are furnished to manage market uncertainty, minimize fraud, and control exposure risk.
CTRM in the Cloud – Research and ReportCTRM Center
The data generated by our survey of the industry suggests that, in general, Energy and/or Commodity Trading and Risk Management (E/CTRM) buyers are increasingly open to considering alternatives to traditional “on- premises” implementation models including both SaaS and hosted in the cloud delivery. While a small, but committed, minority continue to resist anything but the traditional on-premises implementation approach, the overwhelming majority of respondents will consider SaaS/hosted in the cloud for a variety of vertical application areas in and around commodity trading.
Despite that finding, only 16% of those who responded to the survey actually utilize a SaaS or hosted in the cloud E/CTRM solution, and while the data strongly suggests a great deal of interest in the cloud for E/CTRM, it does indicate that the final procurement decision isn’t necessarily a slam-dunk in favor of the cloud. Though 54% of our respondents would consider a SaaS/hosted in the cloud alternative, there are indications that the final decision is still more likely to lean toward a traditional installation on-premises – at least for now. ComTech’s forecast growth rates of 15% per year for SaaS/hosted in the cloud solutions do seem to be reasonable but may accelerate in the future if a sufficient numbers of trading firms adopt the model, are successful with it and are willing to advocate the approach to their peers in the industry. Overall, this finding is in agreement with broader studies such as those conducted by Gartner that found that interest in cloud-based solutions is primarily in horizontal applications such as accounting, HR or billing; and that as a result of buyer concerns around integration and ability to customize, the uptake of cloud-based vertical applications like CTRM lags somewhat.
The environment of physical energy and non-energy commodity trading and marketing has grown increasingly complex, marked by globalization bringing about rapid changes in supply and demand patterns, increased regulatory scrutiny and evolving trading and reporting rules, volatility along the entirety of the physical supply chain, and increasing uncertainty as to future price movements. In order to react to these changes quickly and appropriately, participants in these markets must increasingly rely on a sophisticated infrastructure of software and technologies to ensure a complete view of their trading positions and external market conditions that can quickly and severely impact their values. The core component of these now requisite trading and marketing technologies are energy and commodity trading and risk management (CTRM) systems. As market complexity has increased and multi-commodity trading has become more common, CTRM solutions have had to become more sophisticated and provide a greater depth of capability in order to capture and value the unique characteristics of the multitude of physical commodities being transacted along the physical supply chain, from source to market. Given the capabilities of these CTRM systems, they do represent a significant investment for any trading or marketing organization, generally trailing only the large scale ERP solutions, like SAP, in terms of costs to purchase and implement. Allegro Development, one of the world’s largest CTRM solutions providers, engaged Commodity Technology Advisory to conduct a survey of a number of their clients to determine their views as to the value of their investment and the operational and financial impacts of deploying Allegro’s CTRM solution. This report summarizes the results of that survey and discusses the key considerations for any company seeking to develop their own assessment of the value of their CTRM technology investment via a Return on Investment (ROI) calculation.
5 AI Solutions Every Chief Risk Officer NeedsAlisa Karybina
For the risk manager, AI means greater efficiency, lower costs, and less risk. There are many potential applications of AI when it comes to managing risk in banking, but this report will focus on five key solutions with huge potential ROI that every chief risk officer (CRO) can begin building immediately. Representing foundational capabilities for risk management, these five solutions have the potential to substantially impact a bank’s financial results, and an automated machine learning platform represents the most efficient and effective method of delivering on the promise of these AI use cases.
The theory of comparative advantage, first developed by English economist David Ricardo in 1817, is a theory about the potential gains from trade for companies, countries or people that arise on account of differences in factor endowments or technological progress.
When it comes to scrutinizing costs, most insurance companies can say “Been there, done that. Got the t-shirt.” Managers are familiar with the refrain from above to trim here and cut there. The typical result is flirtation with the latest management trends like lean, outsourcing and offshoring, and others. However, the results tend to be the same. Budgets reflect last year’s spend plus or minus a couple of percent in the same places.
Importance of High Availability for B2B e-CommerceSteve Keifer
This white paper explains how B2B e-Commerce technologies have become so critical to manufacturing and retail companies that further investment is required in high availability architectures.
Capitalizing on analytics in finance: Creating trusted insights for the enter...Spencer Lin
A recent IBM study of 337 CFOs and senior finance executives found that about 90% are implementing analytics solutions. However, analytics adoption is happening in pockets and isn’t pervasive across finance activities. With investment in analytics poised to double in the near term, how can Finance better capitalize on these new capabilities? To answer that question, we looked at the most effective Finance organizations and learned three important lessons from their success.
Automating Payables for the SME Market: Diving Head First into AP AutomationAnybill
This Technology Insight report is for small and mid-sized enterprises with an interest in payables automation. The report includes the latest adoption statistics, current thinking, best practices, strategies, and key performance indicators for evaluating and selecting the solution that meets your needs.
Imagine … internal auditors identifying risks + opportunities from data. Internal auditors can + need to grasp this opportunity to transform their role and industry. More >> grantthornton.com/data-analytics
Discussion of strategies for increasing profits without focusing on expense reduction but instead on areas with leverage like claims. Specific examples for gaining an edge are discussed.
Breaking the data barrier: Lessons from analytically advanced Finance organiz...Spencer Lin
The majority of enterprises recognize that data and analytics are transforming their businesses. But what about the Chief Financial Officer (CFO) and Finance? How are data and analytics changing their professions? To find out, the IBM Center for Applied Insights asked over 1,000 organizations across five industries about their Finance departments’ approach to data and analytics, cloud and engagement. Findings show that enterprises with analytically advanced Finance organizations reported better outcomes when it came to decision making, growth and agility.
Enterprises face unprecedented challenges, and finance is at the epicenter. Increasing business risk and volatility are evidenced by accelerating business disruption through
disintermediation, virtualization and technical innovation. As a result, new competitors, changing business models and changing customer expectations have emerged.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
The banking industry is data-demanding with acknowledged ATM and credit processing data. As banks face increasing pressure to stay successful, understanding customer needs and preferences becomes a critical success factor. Along with Data mining and advanced analytics techniques, banks are furnished to manage market uncertainty, minimize fraud, and control exposure risk.
CTRM in the Cloud – Research and ReportCTRM Center
The data generated by our survey of the industry suggests that, in general, Energy and/or Commodity Trading and Risk Management (E/CTRM) buyers are increasingly open to considering alternatives to traditional “on- premises” implementation models including both SaaS and hosted in the cloud delivery. While a small, but committed, minority continue to resist anything but the traditional on-premises implementation approach, the overwhelming majority of respondents will consider SaaS/hosted in the cloud for a variety of vertical application areas in and around commodity trading.
Despite that finding, only 16% of those who responded to the survey actually utilize a SaaS or hosted in the cloud E/CTRM solution, and while the data strongly suggests a great deal of interest in the cloud for E/CTRM, it does indicate that the final procurement decision isn’t necessarily a slam-dunk in favor of the cloud. Though 54% of our respondents would consider a SaaS/hosted in the cloud alternative, there are indications that the final decision is still more likely to lean toward a traditional installation on-premises – at least for now. ComTech’s forecast growth rates of 15% per year for SaaS/hosted in the cloud solutions do seem to be reasonable but may accelerate in the future if a sufficient numbers of trading firms adopt the model, are successful with it and are willing to advocate the approach to their peers in the industry. Overall, this finding is in agreement with broader studies such as those conducted by Gartner that found that interest in cloud-based solutions is primarily in horizontal applications such as accounting, HR or billing; and that as a result of buyer concerns around integration and ability to customize, the uptake of cloud-based vertical applications like CTRM lags somewhat.
The environment of physical energy and non-energy commodity trading and marketing has grown increasingly complex, marked by globalization bringing about rapid changes in supply and demand patterns, increased regulatory scrutiny and evolving trading and reporting rules, volatility along the entirety of the physical supply chain, and increasing uncertainty as to future price movements. In order to react to these changes quickly and appropriately, participants in these markets must increasingly rely on a sophisticated infrastructure of software and technologies to ensure a complete view of their trading positions and external market conditions that can quickly and severely impact their values. The core component of these now requisite trading and marketing technologies are energy and commodity trading and risk management (CTRM) systems. As market complexity has increased and multi-commodity trading has become more common, CTRM solutions have had to become more sophisticated and provide a greater depth of capability in order to capture and value the unique characteristics of the multitude of physical commodities being transacted along the physical supply chain, from source to market. Given the capabilities of these CTRM systems, they do represent a significant investment for any trading or marketing organization, generally trailing only the large scale ERP solutions, like SAP, in terms of costs to purchase and implement. Allegro Development, one of the world’s largest CTRM solutions providers, engaged Commodity Technology Advisory to conduct a survey of a number of their clients to determine their views as to the value of their investment and the operational and financial impacts of deploying Allegro’s CTRM solution. This report summarizes the results of that survey and discusses the key considerations for any company seeking to develop their own assessment of the value of their CTRM technology investment via a Return on Investment (ROI) calculation.
5 AI Solutions Every Chief Risk Officer NeedsAlisa Karybina
For the risk manager, AI means greater efficiency, lower costs, and less risk. There are many potential applications of AI when it comes to managing risk in banking, but this report will focus on five key solutions with huge potential ROI that every chief risk officer (CRO) can begin building immediately. Representing foundational capabilities for risk management, these five solutions have the potential to substantially impact a bank’s financial results, and an automated machine learning platform represents the most efficient and effective method of delivering on the promise of these AI use cases.
The theory of comparative advantage, first developed by English economist David Ricardo in 1817, is a theory about the potential gains from trade for companies, countries or people that arise on account of differences in factor endowments or technological progress.
International trade is the exchange of capital, goods, and services across international borders or territories.
international trade has existed throughout history (for example Uttarapatha, Silk Road, Amber Road, salt roads), its economic, social, and political importance has been on the rise in recent centuries.
To understand the pattern in international trade, Different trade theories are postulated. Some famous trade theories are:
Mercantilism
Absolute Advantage Theory
Comparative Advantage Theory
Hecksher-Ohlin Factor endowment theory
Product Life Cycle Theory
New Trade Theory
Porter’s Diamond Theory for competitive advantage
Restrictions on imports – tariff barriers, quotas or non-tariff barriers.
Accumulation of foreign currency reserves and gold and silver reserves. (known also as bullionism)
Granting of state monopolies to particular firms especially those associated with trade and shipping.
Subsidies of export industries to give competitive advantage in global markets.
Government investment in research and development to maximize efficiency and capacity of domestic industry.
Allowing copyright / intellectual theft from foreign companies.
Limiting wages and consumption of the working classes to enable greater profits to stay with the merchant class.
Control of colonies, e.g. making colonies buy from Empire country and taking control of colonies wealth.
England Navigation Act of 1651 prohibited foreign vessels engaging in coastal trade.
All colonial exports to Europe had to pass through English first and be re-exported to Europe.
Under British Empire, India restricted in buying from domestic industries and were forced to import salt from the UK. Protests against this salt tax, led to ‘Salt tax’ revolt led by Gandhi.
In seventeenth Century France, the state promoted a controlled economy, with strict regulations about the economy and labour markets
In the modern world, mercantilism is sometimes associated with policies, such as.
Undervaluation of currency e.g. government buying foreign currency assets to keep the exchange rate undervalued and make exports more competitive.
Government subsidy of industry for unfair advantage. China has been accused of offering too much subsidised investment for industry, leading to over supply of industries such as steel – meaning other countries struggle to compete.
Surge of protectionist sentiment, e.g. tariffs on imports.
Copyright theft
The trade theory that first indicated importance of specialization in production and division of labor is based on the idea of theory of absolute advantage which is developed first by Adam Smith in his famous book The Wealth of Nations published in 1776.
Smith argued that it was impossible for all nations to become rich simultaneously by following mercantilism because the export of one nation is another nation’s import and instead stated that all nations would gain simultaneously if they practiced free trade and specialized in accordance with their absolute advantage. Smith also stated that the wealth of nations depends upon the goods and services available to their citizens, rather than their gold reserves. While there are possible gains from trade with absolute advantage, the gains may not be mutually beneficial. Comparative advantage focuses on the range of possible mutually beneficial exchanges.
Adam Smith argued that a country has an absolute advantage in the production of a product when it is more efficient than any other country producing it.
Countries should specialize in the production of goods for which they have an absolute advantage and then trade these goods for the goods produced by other countries
In economics, principle of absolute advantage refers to the ability of a party (an individual, or firm, or country) to produce more of a good or service than competitors, using the same amount of resources.
3+ Keys to Proactive Underwriting (1).pdfCogitate.us
What is the advantage of insurance technology built by insurance
people? It has been designed with a passion to solve problems and meet your needs based on real-life experiences by people who have held your roles. Those who have done the job of producer, underwriter, product manager, CFO, and CIO, know firsthand the functions and features that impact speed to market and your ROI at a granular level. Welcome to
Cogitate and an introduction to future-ready underwriting on a modern
policy administration platform.
Identify emerging trends in defects to reduce warranty and recall
costs, maintain brand strength, and improve customer loyalty
Manufacturers, on an average, spend as high as 2.5% to 8% of their revenue on warranty costs. Best in class
manufacturers have been able to achieve warranty spends as low as 0.5%, underlining the tremendous potential for
cost reduction in business operations - from efficient process management as well as product quality improvement.
Majority of manufacturing companies limit their warranty business intelligence to historical reporting needs.
Manufacturers need robust technology that can monitor & report, provide timely alerts on critical warranty parameters,
predict warranty patterns and improve the analysis of scenarios under different warranty conditions. Presenting
Sailotech’s Warranty Analytics Solution - a rapid deployment, quick returns remedy to current business challenges.
Warranty Analytics solutions use to discover hidden trends in warranty and service records to more quickly identify the
emergence of previously unknown defects. Sailotech can transform data into insights to help you proactively address
your brand equity resulting from product defects while balancing customer satisfaction with profitability.
Article discussing the potential for realignment of insurance strategies to focus on differentiating factors that may or may not include legacy systems replacement. Should legacy systems be outsourced and insurance resources reapplied to strategically unique areas?
Cloud Enabled Transformation In InsuranceCapgemini
Immature capabilities and growing market disruptors are compelling insurers to act swiftly and become fully customer centric. According to the World Insurance Report 2015 less than 30% of customers are having positive customer experiences globally forcing Insurers to reinvent their ability to deliver positive customer experience across the entire customer journey.
Capgemini's ACEs (All Channel Experience) for Insurance is built on Salesforce the leading CRM platform to help insurers improve their core capabilities and enrich customer experiences regardless of customer channel or device preferences.
Find out how Cloud-Enabled Transformation in Insurance from Capgemini and Salesforce is a faster and less disruptive way for insurers to rapidly evolve digital capabilities to achieve customer experiences that leave your customers wanting more!
Digitizing Insurance - Transforming Legacy Systems to Adopt Modern and Emergi...RapidValue
This paper explains how insurers can use the digitization (digitalization) opportunity to deliver greater value to their customers. It is also, revealed how the companies can gain competitive advantage. Insurers are able to engage more intensely with the existing customers and also, attract newer customers with the help of innovative products. Digitizing improves profitability and facilitates growth.
Robotic process automation powers digital transformation in insurance industryArtivatic.ai
The era of robotic process automation (RPA) coupled with deep learning is here. From back-office functions to customer solutions, it has effectively turned processes around on their heads. Leading banks, hedge funds, and asset managers have successfully leveraged RPA tools not only to streamline standard processes but also to save money significantly.
There’s already a pervasive culture of change in the insurance industry. The quest for efficiency gains is a major driver, along with the need to keep pace with evolving consumer expectations and investment in new digital technologies. Robotic process automation (RPA) is a natural fit in this new environment because change can be delivered with speed and agility to realize benefits quickly. Further, RPA can automate the end to end lifecycle by integrating new front end digital technologies with back office environments.
1. 58 Middle East Insurance Review January 2017
Takaful – Motor
Motor up with analytics
The use of data analytics is
crucial in the motor takaful
business in order to restore
higher levels of profitability,
says Mr Muhammad
Ashfaq Ur Rehman.
Empirical evidence shows that there
are takaful companies that have mo-
tor loss ratios of upwards of 120% or
even more, although there are also
examples of lower loss ratios. The point
here is not to necessarily focus on what
the loss ratios are but to highlight the
significance and the negative impact
triggered due to the upward swing in
loss ratios as a result of the unavail-
ability of data analytics.
According to A.M. Best’s special
report published recently, the motor
insurance market share (net written
premium) was 35.5%, and 50.8% was
medical, whereas 13.6% was from other
lines of insurance products in 2014.
Such high values for the motor and
medical businesses demonstrate the
significance of both lines of business.
Leaving the natural or environmen-
tal catastrophes aside, the issue of the
usual losses is pretty much solvable.
In the absence of data analytics, it is
difficult to predict the loss ratio cor-
rectly or price motor takaful with a
justified formula. This issue can be
overcome through the effective use of
data analytics in addition to a robust
actuarial/underwriting model.
Saudi Arabia and the UAE, the
leading takaful markets within the
GCC, are working hard to put in place
the right regulations to govern the
industry and safeguard the interest
of stakeholders.
Why should good drivers be
penalised with the bad ones?
Why shouldn’t a good driver be paying
less premium for motor takaful com-
pared with another who has a history
of accidents? Is it not the bad driver
who is supposed to pay more? A good
driver should have some incentive
over a bad driver if the good driver
is an experienced driver and had no
accidents in the past.
Since some takaful companies do
not have the ability to analyse custom-
ers individually and in depth, thus
they cannot treat each customer as per
what they are due. So they offer flat
rates without any differentiation and
even sometimes a higher rate than the
previous year. These all depend on how
the company is doing on its budgeted
loss ratios for a certain line of business.
In such a situation, as an alterna-
tive, confident drivers who believe that
they would not have any accidents due
to their own negligence or mistakes
during the policy year, may then
choose a slightly higher deductible in
order to get a special rate on the mo-
tor takaful. But that is something one
needs to understand really well and
decide upon carefully.
Random decision-making
leads to unjustified pricing and
high losses
There is a fundamental issue of not
Tak-Motor.indd 58 29/12/2016 5:47:25 PM
2. January 2017 Middle East Insurance Review 59
Takaful – Motor
Highlights
• The smart use of data
analytics could be a game
changer for a profitable
motor takaful business.
making informed decisions for many
takaful underwriters/companies. One
can only make informed decision if he
or she has the relevant data to analyse.
If the necessary data on customers and
vehicles is available and the data is
clean and easily accessible, the com-
pany will have better decision-making
and customers will benefit and stay
loyal to the company for a long time.
Some might arguably proclaim
that they have the requisite data. The
problem with poor decision-making
has been either due to a lack of data
or the poor quality of the data, ie, is-
sues of accuracy and authenticity. Poor
quality data starts with punching in
the details of any customer and vehicle
incorrectly or not in line with the ex-
pected outcome into the system. The
worst-case scenario is that there is no
quality check performed on keying in
the details of customers and vehicles,
therefore the right inference from the
data is questionable.
Substandard work put in at this
point affects both the takaful opera-
tor and some customers in the form
of unjustified pricing and unantici-
pated losses. As a result, the takaful
loss ratio deviates from the projected
figures where good drivers do not get
preferential rates and are charged for
those who happen to have accidents
which is obviously not fair. This may
differ from company to company as it
depends on how analytics savvy the
company is.
Some takaful operators only come
to know of their loss ratios through
their periodical financials produced
by the relevant departments. Why? Be-
cause there is no audited management
information system or data analytics
available on a real-time basis in a
digital format. Tools like business in-
telligence or Qlickview are handy but
they can only help if they are deployed
in their true spirit, and only when the
data coming from core applications is
also accurate and authentic. In some
companies, the motor portfolio holds
the biggest slice of the pie, hence in the
worst-case scenario that would gobble
up the capital of the company quickly.
Data analytics to make motor
takaful profitable
Digital disruption is sweeping through
industries and takaful being already a
laggard can take full advantage of the
digital force which is nearly neutralis-
ing competition across businesses. The
exploitation of data analytics could
be a game changer for a profitable
motor takaful business. Unleashing
the power of data analytics to its full
extent can enable takaful operators to
get the desired results.
Data analytics can provide in-
telligence on customers for better
engagement, enable data mining,
perform predictive analysis to fore-
cast losses more vigorously, perform
retakaful arrangements, predict the
cession rate and help with the choice
on the access of the loss or quota share.
Data analytics can help takaful
operators to stay up-to-date to gauge
the pulse of the business for immediate
and informed decision-making when
taking corrective measures. As a step-
ping stone, takaful operators with the
help of data analytics should analyse
the reality on the ground by searching
answers to the following questions:
• Is the data of customers and vehicles
entered into the system correctly?
• Is the criterion of customer/vehicle
identification robust?
• Is there a clean database, free of er-
rors and incompleteness?
• Has the date ever been audited for
accuracy and authenticity?
• Do we have master level data man-
agement as a centralised database
solution?
• Can data of existing/new customers/
vehicles be fetched from the com-
pany’s own or third- party systems
seamlessly?
• Is the pricing philosophy ad-hoc
and does it take customer relation-
ship (profitable or loss-making) into
consideration?
• Is an eye kept on the industry
landscape at real-time and at close
intervals?
• Is there a historical record of a
specific customer and/or vehicle?
• What is the overall value of any
customer’s relationship to the com-
pany? Are other products taken into
consideration when pricing motor
takaful?
• Is there a rationale for contribution
cession, for the choice on the access
of the loss or/and quota share in
retakaful arrangements?
Conclusion
Already under pressure, takaful op-
erators need to “disrupt” themselves
sooner rather than later. They are go-
ing to be challenged further as they
will soon be receiving enquires to
insure driverless cars. In Singapore,
a trial on driverless taxis has already
kicked off. In the Middle East, Dubai
is also not far behind with driver-
less vehicles, with a test ride already
performed.
Similar to any other industry, data
analytics can literally help takaful
operators to improve their bottom
line performance, not only in the mo-
tor takaful business but also in other
businesses such as medical, property
and travel.
Data analytics built intelligently
will help in enhancing customer
experience, creating cross/up-sale op-
portunities in addition to providing
insight into the business at large. So,
the following steps should be taken:
• Punch in the data correctly with the
“four eyes” principle so that you do
not merely depend upon intuition
while pricing and projecting losses.
• Make full use of data analytics (in
house and third-party solutions)
to provide a snapshot of historical
claims regarding the driver and
vehicles.
• Keep an eye on the dashboard’s co-
lour schemes (green, amber and red
should your company be using full
proof data analytics and business
intelligence tools).
• Use telematics which can help in
getting more intelligence about
behavioural aspects such as speed
pattern, distance travelled, brakes
application, area of travel, causes
of accidents, etc. It is also good to
team up with auto manufacturers
to fit “black boxes” in vehicles under
loyalty programme arrangements.
Mr Muhammad A shfaq - Ur- Rehman is an
independent management consultant who
advises on strategy, distribution, performance
improvement, insurtech, digital transformation,
data analytics, sales, marketing and customer
experience.
Tak-Motor.indd 59 29/12/2016 5:47:41 PM