Business Risk and Analytics

Business, Risk, and Analytics

Introduction

There is a synergistic relationship between business, analytics, and technology (the BAT triangle) that applies to all business, but is particularly powerful in the case of a property-casualty insurance business. This note reviews the functions and concerns of business: profitability, risk and growth, and explains how business naturally requires analytics for planning, product development and product delivery. Technology provides the tools to implement and communicate results between analytics, business management and customers.

Today accelerating technological development is enabling analysis and disruptive innovation that was not possible even a few years ago. New capabilities are having a profound impact on industry generally and on the insurance industry in particular—a topic we cover at the end of the course.

The importance of technological knowledge was emphasized by Jeff Immelt, then CEO of General Electric, when he announced in 2016

If you are joining the company in your 20s, unlike when I joined, you’re going to learn to code. It doesn’t matter whether you are in sales, finance or operations. You may not end up being a programmer, but you will know how to code.

Part of his motivation: a desire to mimic Silicon Valley entrepreneurs. Programming languages and analytic techniques are becoming ubiquitous in business and any well-rounded employee should have a facility to communicate seamlessly across the three inter-related domains of business, analytics and technology. RMI3338 is designed with these needs in mind.

RMI 3388, Computer Applications in Insurance, introduces students to the use of technology within an insurance company. It provides hands-on examples of the types of project a new analyst would perform, as well as a high-level description of the evolving impact technology is having on insurance.

This document served as an introduction to a course called Computer Applications in Insurance (RMI3388/RMI688) that I developed and taught at St. John’s University, in the School of Risk Management, Insurance, and Actuarial Science. It was written in 2018.

Business

What Is a Business?

Business is an agreement now to deliver products or services in the future according to specified terms and conditions. The exchange is generally for money. Customers are often strangers with whom there is no prior relationship, nor is there necessarily an expectation of a future relationship.

Business Decisions

A business person is someone making business decisions: decisions to bind their company to the delivery of certain goods and services to customers according to set terms and conditions. These decisions are where business lie and are what separates a business person from an advising professional or analyst. The analyst may provide input to the business decision, but unless they are making the decision they remain removed from the business process. Sometimes an analyst makes business decisions in their own domain, e.g. to invest to develop a new analytic capability.

The Three Concerns of All Businesses

Businesses have three overriding concerns.

• Profit: that is, the excess of revenue over expenses. The function of a business is usually to make a profit. An interest in profit implies an interest both in revenue and in expense.
• Risk: the certainty with which a business can achieve its objectives impacts the resources (capital) it requires as well as the expense of raising those resources from investors.
• Growth: all companies try to grow. Investors pay a premium for a growing company, in accordance with the Gordon growth (dividend discount) model. Internally, growth offers opportunities for employees, helping make the firm a more attractive place to work.

Profit can be measured in absolute terms in dollars, as a percentage of revenue or a margin, and as a percentage of resources or capital required by the firm as a return on assets or, more commonly, return on equity. In insurance profitability is often measured using the loss ratio, expense ratio, and combined ratio.

Risk is measured using a variety of metrics including volatility of cash flow and net income, deviation from plan, consistency of earnings, and the ability to meet or exceed target earnings. In insurance, where potential volatility may exceed actual volatility, models are often used to assess a range of potential outcomes. Regulatorsating agencies use these models in order to determine the adequacy of capital.

Growth is usually measured through revenue growth and net income growth. From a shareholder perspective EPS, or earnings-per-share, growth is also used. Ensuring continued growth in the long-term depends on a business’s strategy. Short-term competitive threats from inside the industry must be monitored along with the potential for longer-term disruptive innovations.

A business can be characterized as a series of cash flows with different growth, consistency and absolute value characteristics. Larger enterprises typically combine many different businesses with different cash flow characteristics into a single holding company structure. The holding company’s business is the task of financing its positions in its different businesses as economically as possible using a combination of debt, equity, and other financial instruments. This important business function is usually considered distinct from whatever is the day-to-day “business” of the firm.

Analytics

What is Analytics?

Business analytics is an iterative exploration of data, with an emphasis on statistical analysis, used by companies committed to data-driven decision-making. It borrows from, applies and subsumes a wide range of techniques including:

• Descriptive analytics and exploratory data analysis
• Data visualization
• Statistics
• Predictive modeling
• Data science
• Deep learning
• Big Data
• Behavioral analytics

Why Businesses Need Analytics

There is an inherent risk in all business operations because businesses commit now to deliver goods or services in the future. The business must plan, forecast and extrapolate potential demand for its products, assess an evolving competitive market, determine optimal delivery, marketing and sales strategies, and invest and deploy resources in advance of revenue to achieve its objectives. These activities must be based on a rigorous analytic foundation, particularly if the business must to raise external debt or equity financing. As a result, businesses are increasingly reliant on analytics for

• business planning, forecasting and accounting,
• market research, competitive analysis,
• statistical analysis of past data and trends, and
• big data, or perhaps more accurately behavioral data.

These services are deployed in product design, pricing and management; process management; risk analysis; marketing sales and distribution strategy and implementation; as well as strategic management and competitor analysis. Keynes’ “Animal Spirits” play a role in new investment, but they are increasingly supported by rigorous analysis.

When Analytics Is the Business

In many cases analytics is the business, or is a major component of the business. Google, Amazon and Facebook are perhaps the best-known examples of an analytically driven companies. In financial services analytics have overtaken individual human judgments on many critical decision paths.

Technology

The Evolution of Technology

Bill Gates famously said “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.” He called for a personal computer on every desk and in every home, but did not dream of an even more powerful computer in every pocket. Soon the entire globe will is connected through the smart phone network, those without electricity using solar panels to recharge their phones.

Since 2012 there has been an unexpected explosion of activity in machine learning or artificial intelligence. Progress has been facilitated by vastly improved computer hardware, including the use of graphical processing units (GPUs), combined with access to truly massive data sets to train the models. Today technology is evolving material capabilities in quintessentially human skills such as

• Comprehension: moving from a voice recording to voice control, Siri, Alexa; from image capture to image recognition: separating the muffins and from the dogs.
• Perception: a contextual understanding to detect mood, stress, danger, anger, delight; e.g. fraud detection and prevention, automated escalation during client service phone calls.
• Prediction: synthesizing and understanding a whole scene to predict what will occur next; e.g. a driverless car predicting a potential accident and adjusting course

New comprehension, perception and prediction based technologies are being applied in many industries, including financial services and insurance. Underwriting for personal lines and small commercial accounts is increasingly automated, relying on behaviorally-based big data sources to estimate an insured’s underlying loss propensity. As the technology becomes more mature, the hardware cheaper, and the data sources more extensive these trends can only continue in the future. Today, insurance executives need familiarity with key concepts in analytics, technology, machine learning and big data in order to chart a sound strategic course for their firms.

Hardware and Software

Technology consists of hardware and software.

Many of the successes of computers result from plummeting hardware costs, costs driven down by economies of scale in a virtuous higher-production, lower-cost cycle. It is estimated there are 100,000 mainframe computers, 1.5 billion personal computers, and over 5 billion smart phones. More Indonesians have a cell phone that have access to reliable electricity. A recent iPhone launch sold 25 times more CPU transistors that existed in the world in 1995. As a result of scale hardware has become a low-cost commodity, with cloud providers taking over functions historically managed by a business themselves but with radically lower costs, faster response times, and almost infinite scalability.

Programming creates software that makes hardware come to life. Programming languages vary according to the demands they place on the user, their flexibility and the control of hardware they offer. Lower level languages have high demands of users but allow a lot of control and can be used to create very tailored solutions. Higher level languages are often domain specific and highly optimized but sacrifice flexibility for speed and ease of use. Examples of lower and higher level languages include

• Assembler (lower level)
• C/C++
• Python
• R
• SQL (higher level)

The creation of software has been simplified and made more efficient by access to vast software libraries, often at no cost, simplifying the creation of new programs. Google has billions lines of code internally, in a carefully curated and indexed repository; coding often becomes a matter of finding pre-existing solutions and gluing them together with simple scripts. As a result programmers today can be orders of magnitude more efficient than programmers ten and twenty years ago. A great example of technology building under this new paradigm is WhatsApp. Incorporated in 2009, by 2013 it was serving 200 million active users with only 50 staff members.

Technology Enables Analytics

Technology—the combination of various forms of hardware and software—enables analytics: data gathering and storage; algorithmic computation; distribution and communication of results. Broadly technology can perform a wide range of functions, which can be grouped as follows.

• Memory: databases and processing, real-time access to data, centralized control of data. SQL databases, not-SQL databases, CSV files, tables, tidy data
• Computation:
  • Easy computation: an extension of arithmetic that could be performed by hand, often in a spreadsheet, e.g. accounting, basic actuarial work
  • Hard computation: seven-day weather forecast, catastrophe models, blockchain, and encryption all require an overwhelming number of computations which would be impossible without modern computing devices. Generally requires a programming language.
• Communication: computers enable two-way communication, especially via the Internet, data capture and the creation of text, documents, media, videos and images that summarize, synthesize, and condense information.

Technology Led Disruption

Evolving technology has disrupted many industries. According to Clayton Christensen’s classic model the process starts with a seemingly inferior product that is ignored by incumbents. They focus on building feature-rich products for their most demanding customers. These products over service mid- and lower-tier customers. Incumbents fail to recognize the degree to which the innovation will improve over time and are not prepared when they improve enough to threaten their top-tier customers. Digital cameras are a classic technology-led disruption. Today there are hundreds of InsureTech startup companies trying to disrupt insurance. Potential disruption of the insurance industry could come from innovations based on many technologies:

• driverless cars,
• disintermediation, directly accessing customers,
• disaggregation, directly accessing capital providers,
• peer-based insurance,
• remote monitoring, e.g. home sensors,
• drones,
• satellite imagery,
• usage-based insurance, particularly for automobile, or
• the sharing economy.

Insurers are acutely aware of the strategic threats these innovations pose to their businesses. Many are responding by investing in start-up companies in order to be on the inside of future innovations. Designing and implementing an appropriate strategic response to technology-led disruption is high on the agenda of senior managers throughout the insurance industry.

Technology Threats

Technology has a dark side, attested by recent election tampering, Equifax data breach and cyber attacks like the WannaCry ransomware. For the insurance industry cyber represents both a threat to operations and an opportunity to grow and sell more. Cyber risk is high profile, and a growing list of senior management casualties has created an urgent senior management-led demand for cyber liability insurance. The insurance industry is struggling to develop the required analytics to quantify aggregation risk and determine suitable pricing for cyber policies. However, even absent robust analytics, the cyber risk market is growing very quickly and is projected to be a $10-20 billion insurance line by the end of the decade.

The BAT triangle

Business, analytics, and technology (BAT) lie in a symbiotic and mutually reinforcing triangle, Figure \(\ref{battri}\). The power of analytics is deployed to solve a variety of business problems. Low-cost hardware allows efficient and distributed implementation of sophisticated analytic models that can, in turn, be communicated to the businesses and to their customers. As customers use and interact with the solutions the business gathers more data, which can be used to enhance and fine-tune the analytic models, and so the circle continues. Ideally, here is how the BAT symbiotic triangle works:

• a business problem, leads to
• an analytic model, which is combined with data and is enabled through
• technology, which in turn enables communication of the results back to business decision makers.

Figure 1: The BAT triangle.}\label{battri

Despite the obvious synergies, linkages, and relationships between the three corners of the BAT triangle, internally companies often struggle with hierarchical organizations and function-based silos. Each of the three functions tends to attract employees with different skill sets, ambitions, and modes of working. As a result there can be inefficiencies at the interface between the business and analytics services, analytics and technology, and technology in the businesses.

Silicon Valley entrepreneurs speak to the power of sitting at the intersection of the BAT triangle. These individuals usually have a keen appreciation of all three dimensions of their nascent businesses. In too many legacy businesses, however, the relationships are more muddled and the BAT triangle degenerates:

• between business and technology sits the business analyst, who often attempts to translate between the two without a real appreciation of either;
• between analytics and technology: unclear and incomplete specifications flow from analyst to technology, or technology asks analysts to “Tell me what you want” of analysts who have no idea what is technologically possible;
• between business and analysts: a view from the business that analysts are an impediment to getting business done and a from the analysts a lack of appreciation for the realities of business.

Fundamentally these are problems of education and awareness, which RMI3388 will help address.

Insurance Industry Background

The insurance business can be divided into three segments

1. Life: the sale of life insurance and annuity products. Life products are typically characterized by their long duration, with contracts often surviving decades. Life products include a significant investment component, which is generally not present for property-casualty covers.
2. Health: health insurance typically combine substantial administration, price negotiation, network and service provider management functions. These are often disaggregated from risk transfer and sold separately. Health insurance straddles life and property-casualty covers depending on its structure: the recent political debate about pre-existing conditions is really a question of whether a health policy is a one-year policy or a whole life policy.
3. Property-casualty: products cover liability and property risks, on a first- and third-party basis, as well as specialty lines including credit, fidelity and surety. Property-casualty products are almost always written on a six month or one year basis and are not guaranteed renewable. They are short duration contracts.

RMI 3388 will illustrate computer and technology applications within the property-casualty insurance business.

The US Property-Casualty Insurance Industry

The US property-casualty industry wrote $642 billion of gross written premium in 2017, and, after reinsurance, net earned premium was $546 billion. Business is split almost evenly between personal lines and commercial lines. Personal lines are dominated by private passenger automobile, accounting for 40% of overall premium. Homeowners accounts for a further 6% of premium. The largest commercial lines are liability, including general liability and specialty liability, workers compensation, commercial multi-peril, fire and allied lines, and commercial automobile.

In 2017 the industry combined ratio was 104%, up three points over 2016 because of higher catastrophe losses. The 104% is comprised of a 76% loss and loss adjustment expense ratio and a 28% expense ratio. The industry return on invested assets was 3%, it earned $50 billion of net income, and its return on equity was 5.5%. Statutory insurance companies paid $30 billion in dividends to their stockholders. These statistics are all available on the snl.com website, which you have access to as a St. John’s student.

The risk of the insurance industry was supported by $767 billion of capital, part of $1.9 trillion of assets. The largest single liability was loss reserves of $647 billion.

Insurance Company Functions

In order to understand what services the property-casualty industry provides it is instructive to look at the distribution of their expenses. Total expenses, including an average cost of capital, are nearly 50% of premium, corresponding to a spend of over $250 billion per year.

• Underwriting expenses are 28% of premium;
• Loss adjustment expenses are 12% premium, these are included in the 72% loss and loss adjustment expense ratio quoted above; and
• The average cost of capital is about 10% of premium.

Underwriting expenses can be broken into customer acquisition and management costs, such as sales, acquisition, and needs analysis, marketing, and education, totaling 20% of premium, or $100 billion; and costs associated with providing regulated insurance paper, such as underwriting, product management, regulatory and compliance costs, taxes licenses and fees, billing, policy maintenance and policy issuance, of nearly 10% of premium, or $50 billion per year.

Annual Insurance Company Expenses, USD billions.

Activity	Expenses	Activities
Customer	100B USD	Marketing, sales, education
Paper	59B USD	Maintenance of regulated insurance paper, product design and pricing, customer servicing
Claims	69B USD	Claims adjustment services
Capital	45B USD	Cost of capital required for risk bearing
Total	273B USD

Table \(\ref{activities}\) summarizes how insurance company operations can be divided into the four categories related to customer, paper, claims and capital.

How Is Technology Used Within Insurance?

Insurance companies have become increasingly reliant on technology in all areas of their operations. As a result today it is fair to say that each of the four activities, customer, paper, claims and capital rely on all of the major functions of technology we have identified: databases, easy and hard computations, and communications. Technology is used everywhere.

Example Uses of Technology by Insurer Function

Activity	Database	Computation	Communication
Customer	Customer and policy records	Purchase propensity and needs prediction	Marketing
Paper	Regulatory compliance, underwriting	Credit scoring, catastrophe modeling	Financial reporting
Claims	Claims process management	Fraud detection	Claim reporting and adjusting
Capital	Portfolio management	Investment portfolio optimization	Investor relations

Modules and Learning Objectives

RMI3388 is focused on the insurance business. The course has four basic modules.

1. Profitability: determine profitability by computing ultimate loss ratios.
2. Communications: explore and implement different ways to communicate analytic concepts clearly and concisely using tables and charts.
3. Risk: measure catastrophe risk using a catastrophe model and measure risk using PML and other risk measures.
4. Strategy and Growth: Understand the strategic challenges technology poses to insurers today through an in-depth discussion of blockchain and Bitcoin technology and an overview of the InsureTech start-up universe.

These modules track with an insurance business’s focus on profit, risk, and growth, as well as the need to communicate effectively.

The description of RMI3388 says it will

Provide students with hands-on experience in different computer software to perform various data analysis tasks that are commonly required of entry-level jobs in insurance industry. Basic and intermediate statistics concepts are reviewed in the context of insurance applications.

RMI3388 is a multi-disciplinary course, combing technology skills with statistics, accounting, actuarial, underwriting and insurance company operational analytic skills. As a result, the course’s learning objectives can be technology-based, or statistics-, actuarial- or model-based, or both. For example, to quantify profitability the course introduce loss ratios, earned premium and loss development. The relevant actuarial concepts, such as the construction of link ratios, will be introduced through a hands-on spreadsheet implementation. It is not productive to try to separate technology learning objectives from model or statistical ones, and no attempt to do so is made below.

Profitability Module

Motivating Business Problems

• How should I measure and compute profitability in insurance?
• What is the performance of my book of business?
• Brief discussion of expense ratios at combined ratios

The analysis of loss ratios, loss development triangles and link ratios will be performed in a spreadsheet. We will use data from each student’s selected company and from the CAS Loss Reserve Database.

Learning Objectives

• Identify and use the different components of a spreadsheet (menu bar, formula bar, body, sheets, sheet tabs, status bar)
• Ability to layout a simple spreadsheet
• Entering basic arithmetic formulas in cells
• Layout, formatting, column headings and titles for a simple table of estimated ultimate loss ratios given premiums and losses
• Define and recognize data types including: string, date, integer, floating point number
• Recognize and apply appropriate formatting for each data type
• Appropriate use of cell alignment (horizontal and vertical) in a spreadsheet
• Appropriate use of cell color, shading, border colors, font face, size and weight (or generally, resisting the urge to over-format)
• Meaning and appropriate use of absolute row and column addresses (e.g. =$A$1, =A\(1, =\)A1)
• Use of built-in spreadsheet functions such as SUM, AVERAGE, MAX, MIN, STDEV
• Use of built-in spreadsheet logic functions including IF statements
• Use build-in spreadsheet date functions, e.g. to split a date into month and year parts, or compute the difference between two dates as a fraction of a year
• Recognize and list types of built-in spreadsheet functions including string functions, date functions, mathematical functions, the statistical functions, database functions
• Ability to use spreadsheet help, in conjunction with knowledge of function types, to find and use new functions
• Creation of simple charts within a spreadsheet, including XY charts, line plots, and bar charts
• Familiarity with and appropriate use of spreadsheet named ranges
• Define written premium and earned premium
• Given premium and losses define and compute a loss ratio, explain its importance to an insurance company
• Given a loss ratio determine if it appears high, low or in-line with expectations and explain why
• Compute earned premium from a transactional history of written premium (premium, effective date, expiration date)
• Define and identify stages of the claim settlement process (incurred, reported, open, closed)
• Define and identify different types of loss (paid, case incurred, incurred, case and bulk or IBRN reserves, ultimate)
• Interpret incurred loss correctly from context
• Explain why RBNI is a funny actuarial joke
• Compute paid, case incurred, bulk and ultimate losses given transactional data or appropriate loss development data or from basic algebraic identities
• Describe why bulk loss reserves are important and how they are incorporated into published financial reports
• Define and compute accident year and calendar year losses from transactional loss data
• Define and identify the components of a loss development triangles, and list reasons for claim loss development both for an individual claim and a portfolio of claims
• Create a loss development triangle from transactional claim data (date, event such as open, reserve change, paid and its dollar amount)
• Define and compute link ratios given a loss development triangle
• Estimate average link ratios using standard straight and weighted averages
• Combine individual link ratios to compute factors to ultimate
• Develop losses to ultimate using factors to ultimate derived from link ratios
• Describe the concept of a reasonable range and use a loss development triangle to compute a reasonable range of ultimate losses
• Describe favorable and adverse loss development and explain what they signify for company management
• Compute favorable and adverse loss development from a loss development triangle and booked ultimate losses
• Compute estimated loss ratios from appropriate inputs
• Ability to summarize a loss development analysis in a management-ready exhibit
• Given a range of different types of premiums and losses select the appropriate ones to use to compute loss ratios

Communications Module

Motivating Business Problems

• How can I communicate technical results using computer generated tables?
• How can I communicate technical results using computer generated charts?

The Communications section will use a series of case studies based on real-world insurance related and other financial and demographic data. The objective of each study will be to find and communicate a message in the data. Some case studies will be worked interactively in class and others will be assigned as homework. Each student will present one finished exhibit to class.

Case studies will be analyzed using the many different tabular and graphical formats available in Excel.

Learning Objectives

• Ability to define, describe and recognize tidy data
• Ability to transform messy data into tidy data using pivot tables and other Excel data functions such as vlookup, match and sorting
• Create appropriate derived data fields, including cumulative sums, percent of total, lagged values, etc.
• Transform data to facilitate creation of various plot types
• Describe the weaknesses of spreadsheet as a data manipulation platform and awareness of alternatives such as SQL
• Data summarization using mean, standard deviation, maximum, minimum, interquartile range in a spreadsheet
• Creation, sorting, and filtering of tabular data in a spreadsheet
• Creation of derived fields, such as day of the week, month or year from the date, appropriate ratios, etc. in a spreadsheet
• Reading and parsing CSV files in a spreadsheet
• Creating and interacting with pivot tables in a spreadsheet
• Basic and intermediate plotting using Excel graphs
• Identify different graph types available in Excel and ability to select most appropriate for particular application
• Ability to identify different components of a graph, including axis, ticks, tick labels, titles, lines, data points and to select appropriate options
• Advanced chart creation including editing of major chart components in a spreadsheet
• List and apply criteria to determine whether a chart or a table best communicates the message
• List and apply considerations for table design (c.f. Few)
• List and appropriately edit components of a table, including title, column headings, row headings, clear labeling of units and sources in a spreadsheet
• List and appropriately edit components of a chart, including title, axis title, tick marks, axis scale, line and bar elements, marker elements, color, shading, legend creation and placement (c.f. Tufte) in a spreadsheet
• Meaning and appropriate use of regression fit lines, moving averages, and other smoothing techniques in charts in a spreadsheet
• Appropriate use of combination charts (e.g. bar and line) in a spreadsheet

Risk Module

Motivating Business Problems

• What is the largest possible loss from my book of business?

Catastrophe risk management is an essential part of insurance company operations. It pervades all aspects of the company’s management and is scrutinized by internal and external stake holders. In this module we focus on how hurricane risk is assessed and managed in an insurance company. The module will introduce students to modern catastrophe models. Students will become familiar with the standard modeling terminology, and be able to perform simple calculations given model output.

The idea of simulation will be introduced using a simple model of the 2016 Presidential Election, translated into insurance terms. It will then be extended to a realistic model of US Hurricane exposure. The model will be tested against historical experience.

Learning Objectives

• Explain what distinguishes a catastrophe loss and a non-catastrophe loss from an insurance company perspective and list the reasons they are so significant a concern for insurers
• Describe the US historical hurricane experience, including average frequency of events, the Saffir Simpson scale, list major landfalling hurricanes since 1900 and the approximate damage they caused
• Enumerate the factors used to describe hurricane intensity: radius of maximum wind speed, forward speed, central pressure
• Explain the concept of simulation to determine mean losses and other statistics of the loss distribution
• Describe the event, hazard, vulnerability, and financial components of a simulation model and explain how they inter-relate and are combined with exposure information (note: course will be limited to a ground-up view of loss, with no coverage modifications)
• Enumerate the factors impacting property hurricane loss vulnerability
• Define frequency and severity components of loss in relation to catastrophe losses
• Describe and apply simple procedures to fitting a model to historical experience (limited to frequency with a Poisson distribution and severity with a lognormal or Pareto distribution)
• Describe how a catastrophe model determines loss for an individual policy
• Describe how a catastrophe model determines loss for an insurance portfolio
• Implement a simulation model in Excel using a What-If table given frequency, number of years and event severity distribution
• Define PML and value at risk
• Define, recognize and use an event loss tables (ELT) to compute average annual loss and standard deviation of loss, and PMLs, including computations in a spreadsheet
• Define, recognize and use an yearly loss tables (YLT) to compute average annual loss and standard deviation of loss, and PMLs, including computations in a spreadsheet
• Given a single loss event and an ELT quantify return period of loss size; determine if loss is an outlier relative to model predictions
• Define and distinguish between occurrence and aggregate catastrophe protections (reinsurance)
• Given an ELT compute the impact of an occurrence protection on AAL and PMLs, including computations in a spreadsheet
• Given an YLT compute the impact of an aggregate protection on AAL and PMLs, including computations in a spreadsheet
• Enumerate and explain the strengths and weaknesses of VaR and SD as risk measures
• Explain and compute an example to show that VaR is not sub-additive and explain the significance for risk management
• Use simple model of US Hurricane to estimate industry PML points; compare to company reported risk tolerances
• Describe how A. M. Best Capital Adequacy Ratio evaluates catastrophe risk
• Given multi-year loss experience assess whether ELT appears consistent with data or not; suggest possible improvements to frequency or severity components

Classwork and Exercises

• Download and analyze NOAA event files
  • Compute annual frequency by state of landfall and Saffir Simpson category
  • Fit Poisson model to event frequency and test adequacy of fit
• Fit lognormal/Pareto to event severity and test adequacy of fit
• Apply and assess different trend procedures to losses to allow for inflation, social inflation and economic development
• Create simple R simulation model for US hurricane
• History of US catastrophe losses from other perils including earthquake, flood, tornado, brush fire, terrorism

Strategy and Growth Module

The strategy and growth module has two parts:

1. A technical introduction to blockchain technology and the Bitcoin distributed currency
2. A discussion of potential technology-driven disruption of insurance starting with blockchain applications but also including other InsureTech startups. Cyber risk will be evaluated as a threat and an opportunity for insurers.

Blockchain

The Bitcoin/blockchain module will try to demystify both concepts. We will introduce hash functions (including discussion and analysis of collision probabilities motivated by the birthday problem) and public key encryption and describe how documents can be digitally signed. Potential applications to insurance, e.g. title insurance, will be discussed. Real-world computations will be performed using a spreadsheet.

We will investigate the Bitcoin block chain and identify specific transactions.

InsureTech

The final course assignment is a group paper and presentation on InsureTech. Students will summarize current InsureTech startup activity. They will then create and administer a survey of friends and peers to identify weaknesses and opportunities in the design and delivery of insurance produces. Based on survey results each group will focus on a particular aspect of disruption, providing either their own ideas or summarizing the approaches of 3 to 5 new startup companies. Each group will write a short paper and present their findings during the final session to the entire class.

Learning Objectives

• Describe and give examples of hash functions and explain their importance in computer cryptography, encryption and security
• Estimate collision probabilities for hash functions as an extension of the Birthday problem
• Describe how hash functions can be used to ensure document integrity and how they are used in password systems
• Describe the main components of a public key encryption system
• Describe the Bitcoin blockchain syntax and its major components
• Describe the meaning and use of a Bitcoin address
• Describe the process by which a Bitcoin is transferred from one owner to another
• Explain the meaning of a nonce
• Explain the proof of work concept used in Bitcoin and its importance to blockchain integrity
• Describe the main components of the Bitcoin ownership and transfer of ownership model and list ways in which it could be applied to insurance
• List the defining characteristics of a blockchain and a distributed ledger, broadly explain its implementation, and list ways in which it could be applied to insurance
• List the attributes of a Clayton Christensen’s disruptive technology, and give examples
• Explain ways that technology is disrupting the core components of the insurance industry and give examples of each
• Demonstrate familiarity with the InsureTech startup universe, through a conversational knowledge of 5 to 10 startup companies
• Demonstrate familiarity with insurer strategic planning to address disruption, including examples of specific company actions (e.g. starting an incubator or an investment fund)
• Demonstrate ability to converse intelligently on InsureTech with insurance executives

Illustrations Using Specific Company Results

Each student will select a US-traded public property casualty insurance company at the start of the course. They will use public information about their company, from 10K and 10Q reports, investor presentations and financial supplements, to illustrate the concepts we study.

Use of Individual Company Reports to Illustrate Business Problems Addressed in RMI3388

Business Problem	Company Illustration
Determine profitability	Reported loss development triangles, including supplements, reported case and bulk reserves, loss ratios by line and in the aggregate, combine ratios
Communication	Investor presentations
Measure risk	10K risk disclosure and catastrophe risk disclosure, including supplements
Strategy and growth	10K risk disclosure, investor presentations, research into insured tech investments

Basic Computing Background

Students are expected to understand the basic operation of computers (e.g. from CIS1332). They should be able to

• Describe the differences between drives, directories and files
• Be able to identify and location the major directories found on a Windows computer
• Write down the location of the My Documents and Downloads folders
• Parse a filename into drive, directory path, filename and file extension
• Explain the meaning of the special directories . and ..
• Explain the meaning of a back and forward slash, including their special meanings in Windows and Linux/Unix/Apple in directories and as escape characters
• Explain which program is likely to open a particular file given its filename and extension
• Map the extensions XML, HTML, XLSX, XLSM, XLSB, DOCX, PPTX, PPTM, BMP, JPG, JPEG, PNG, MP3, CSV, R, Rmd, TXT to file types and list the associated programs
• Recognize HTML, CSV and JSON file formats and list the main features and uses of each
• Create a new folder and sub-folder tree using Windows Explorer
• Download a file from the Internet, store it in a specified location, write down the full path to the file location, find the file in Windows Explorer
• Describe and create a simple CSV file, with column headings and data elements
• Describe the difference between ASCII and Unicode and give examples of why it is important
• Use Notepad to create a simple CSV file in a particular location, read it into Excel
• Download something from web, save it in the right place, with Unicode and sort it out!
• Use of Snipping Tool for screen capture

To the extent students are not familiar with these items we will review quickly in class.

Items Not Covered

• Programming, e.g. no Python, R, C/C++, Java/Javascript
• Visual Basic for Applications (VBA) will not be covered: it is no longer supported by Microsoft and is not a good first language for students who have never seen programming before
• PowerPoint will be used for presentations but not treated in depth. We will focus on creating single tabular and graphical exhibits, and students will review the investor presentations of their chosen public companies and comment on their presentation formats
• SQL, though we will cover basic data manipulation in a spreadsheet

Figure 1: The BAT triangle.}\label{battri