LM3 - C4 - Pricing
RDS and importance
Lloyds defined compulsory RDS + Syndicate defined scenarios
Purpose + value of CAT modelling
deterministic and stochastic approaches to CAT modelling
4.1 Statistical Theory in Underwriting
Past Experience - individual perspective + Insurers collective knowledge + Market dynamics
Frequency & Severity of risk
How often VS how extensive
Low frequency don't provide same quantity of data
Attractional: high frequency low severity
Large: mid frequency mid severity
CAT: low frequency high severity
Arithmetic Averages
- Mean - adding all values divided by total number of individual values
- Median - arrange values in ascending order find 1 exactly halfway, if even take mid point
- Mode - values most frequency in a set of data.
- BEST WHEN LARGE OUTLIERS ie large vs small claims
- Mode ignores edge cases compared to median
- Expression of preference / time taken to perform tasks
Variance in Data
"Normal distribution"
Standard deviation, volatility (Predictable) underwriters should price this in
Stable and predictability
Frequency distribution
Average time to settle
Probabilities & Return periods
the law of large numbers
How likely is it to happen?
Option 1 is where all the outcomes are known and all equally likely, e.g. choosing a particular card from a pack or throwing a six using a die.
Option 2 uses the concept of relative frequencies
OR expressed in bands as figures
"how many collisions a ship owner has as an overall percentage of all the ship’s losses"
Option 3 uses subjectivity as the underwriters (Experience).
Return period:
Probabilities in that they are used to express how often a particular event is expected to occur, e.g. a certain severity of flood.
remain the same!!!
A once every ten years has a 0.1 or 10% chance of happening in any one year.
Problems occur when event happen with greater frequency than expected!
Protection from Less frequent/more CAT event
Understand reinsurance program BACK to FRONT
Importance on the capital of the BOOK
"stochastic models based on larger datasets to generate a large number of scenarios"... consider how claim frequency, timing and values
What can the BOOK handle!!!
Interpretation and use of statistics
Historic data losses value if:
- Primary business to writing excess business
- Widening or Narrowing Cover
- New territories
- Risk type
Client Data
Know the Client.
- What is the client’s reality?
- Is business difficult for them?
- Might that mean that savings will be made on routine maintenance, thus creating a greater exposure for the insurers?
Deterministaic vs probalilitis
CAT modelling RDS
EML of a site is X
Reinsurance
4.2 Pricing
Common pool represents the degree of risk they bring and that is fair
Written premium: Premium before deductions
Signed Premium: Signed proportion premium before deduction
Gross Premium: Signed Premium before deductions or reinsurance
Net Premium: Gross Premium less deductions
Earned Premium: Premium Earned during policy period or "LIVE" for reserving. (Assume linear BUT CLASS dependent)
Incurred Losses: Total paid losses and outstanding loss reserves
Premium for the risk including loading for expenses or costs of acquisition.
- Risk Premium
- Expenses of the business
- brokerage, commissions,
- Return on capital
Risk Premium cover the cost for losses
Claims might arise some time in the future subject to inflation (RPI base + escalation)
Liability classes are long tail vs short tail PD/property
Frequency and severity for different types of potential claims
- What is insured?
- Exposure size?
- Cover being offered
- Particular rating factors (Loading/Discounting)
- Historic claims experience
- Cat Claims
- Unknown market changes (Miss factors)
Cost of claims, climate change, state of the economy
Emerging risks = Cyber
Expenses: Staff , Regulation, Overhead, Office space, Lloyds fee
Electronic placing and cost reduction: Acquisition + operating cost
can pull specific info from white json?
Profit and return on capital employed (ROCE)
Measures how well company is generating profits from its capital.
claims + expenses net of reinsurance <100% of premiums therefore VE + combined ratio
Capital reserving + dividends
Pros and cons of underwriting strategy
focus on investment incomes deplete claims slowly
Calculating the premiums
Technical price for a risk.
Over-supply of insurers will force down price
How to price less then competitors + Justifiable and Run without a loss.
How will you meet business plan?
Burning cost
AMOUNT of PREMIUM in paying losses over a period of time.
Using past claims data of values that don't change much.
inflation factor?
covered offered
- Analyse claims data and project ULTIMATE NET LOSS for any YOA
- Index historic claims to current day values
- Factor in POTENTIAL for LARGE LOSSES
Triangulations?'
Only work if no change in underlying risks or legal framework.
Construction premium and paid claims triangulations
Reserving number:
- raised from claims on Known information
- IBNR = "padded/fat" for reserving
- IBNER = Claims known but reserve entered NOT high enough
Paid Claims...
Outstanding Claims...
Incurred losses...
Rating models
Generic risk data and rating parameters
Feed Known information about individual risk into a framework
Consider number of variables OUTPUT a rate for the risk.
Regulatory oversight of pricing
Setting realistic pricing which are justifiable and support business plan.
Principle 1 - Underwriting profitability
Evaluate sustainable technical pricing
Rate adequacy
deliver sustainable profit
Risk Appetite: Make a return on capital! (baked in ULR)
Region providing enough premium?
Pricing policy sets out how pricing is carried out for risks, SUPPORTS business plan, managed across the cycle... Causes TP to below 100%!!!
Return period discussion
(Brokerage, admin fees, to calculate adequate rate)
Pricing methodology
taking into account Claims:
Attritional
Large
Cat
Pricing decision = 1. experience or 2. exposure data (benchmarks or judgements)
Rational needs to be tracked
Validation of models!!!
Peer reviews done of pricing decisions
Pricing adequacy + rate change management/monitoring
Adequacy of prices + changes in rates charged on a risk between 1 year and another.
Lloyds Performance Management Data Return:
- premium volume summary, including acquisition costs; •
- new risks, including both the benchmark and actual price;
- renewed risks, including the changes in pricing on renewals; and •
- non-renewed risks.
Gradually increasing premium towards the Target Price
Year on Year risk performance
Hotel example... consider TOP3 sites RARC
Price & Rate Monitoring, Audit and Review
Independent reviewers, actuaries, business planning
...
