Use Case

Insurance Pricing

Problem Statement

  • An Asian life insurance company wanted to launch an internet insurer in the minimum amount of time
  • Because it had not been done in that country before, data was sparse
  • There was a need to update pricing models regularly, beginning with no data and slowly improving with each iteration
  • Programming resources were limited

Radar Solution

  • Sue allowed the Underwriter himself to create a model based only on his expert judgement.
  • As time progressed he refined the model using incoming data without interfering with operations or using a programmer.

Metrics Gathered

  • Initially no data, only expert judgement.
  • Later Sue used Age, Gender, Marital Status, Income Level, Smoker? and weight as variables.

Success and Improvements

  • The system performed from day 1
  • The expert opinion model deviated from the final model by less than 1% premiums overall

Implementation Timeline

  • Ongoing

Radar Similar Possible Use Cases

Monitoring of new machinery where there is no data, Credit decisions, targeted marketing, fitting of financial profiles to products, application selections

More Use Cases

Monitoring IOT/Telemetry

Sue can convert any telemetry from a reactive rule base to a predictive model that will avoid unplanned stoppages and nip problems before they become critical. Sue is the ideal AI for IOT. With the vast amounts of telemetry being generated by IOT, there are not enough experts and engineers to read it all. Sue can read every telemetry reading and email / autocall /  refertohuman when action is needed. IOT, Mining, High Precision Farming, Telcoms, Vehicle Monitoring

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Automatic Renewals

Sue can approve renewal of risks and bills risks within fuzzy thresholds. Underwriting, Utility Bills

Data Enhancement

Sue can predict missing data points in a set using the other data.

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Auditing

In any transactional environment, Sue can read every single transaction and flag outliers and exceptions for human review including fraud, money laundering or pricing. Sue is able to imitate an expert that reads a page of transactions and spots the fraudulent one. Except that Sue can read ALL the transactions in a large organization and never needs sleep. Telecoms, Banking, Finance, Triage of Insurance Claims

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Scorecards

Sue helps organizations create and deploy customised scorecards by enabling internal staff to transform their expertise into actionable insights quickly and efficiently. Sue facilitates the development of predictive models that improve decision-making accuracy, align with strategic goals, and ensure consistent performance across the organization.

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Bias

By creating virtual experts that can be deployed in any operational environment, Sue ensures consistent and accurate decision-making, reducing human bias and aligning outcomes with strategic goals.

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Call Centres Next Best Move

Sue can recommend the next best move based on what is put in on each screen, standardizing service and optimizing flows.

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Medical

Sue can learn any individual decision in the medical field and replicate it

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Trading

Sue is being used to analyse specific metrics, economic indicators, weighted risk parameters and soft input to take shareholding decisions ending in Buy/Hold/Sell. Transaction Pricing, Shares, Currency Trading, BlockChain Smart Contracts, Actutarial Pricing Methodology, Fitment of Statistical curves, Reserve Forecasting

See BlockChain Use Case
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Cyber Risk

Sue can act as a help desk able to run set scripts 24/7 anywhere in the world, detect data breaches and protect database by running scripts and smart approving every db/os action

Decisioning

As regulations change, regular slow-moving modeling is unable to keep up. Sue enables companies to make informed decisions, align with strategic goals, and reduce human bias through a straightforward graphic interface, making expert decision-making accessible and consistent across the board.

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Targeted Marketing

You don’t need Cambridge Analytica to target advertisements or profile clients. With a few pieces of information Sue can segment your whole population and send each person the right message. Investment, Retail Deals, Politics

Onboarding Questionnaires

Most AI engines need 80 pieces of information to fire, leading to client fatigue. Sue can give an initial approval after as few as 3 parameters. Credit cards, Employee applications

See Use Case

Application Selections

Unlike humans, Sue can review every single item offered and give an initial unbiased opinion Underwriting, Risk Selection, Recruitment Selection, Holiday Authorizations, Exploration Predictions

Staff Predictors

Sue can read the psychometric results of a candidate and give you the probability of success in a division based on the results and performance assessments of other candidates.

Churn

Sue helps organizations reduce churn by enabling internal staff to create and integrate predictive models quickly and effectively. By empowering client retention managers to identify and address high-risk contacts early, Sue improves churn metrics and supports the expansion to new models within a short implementation timeline.

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