- An insurance client had a schedule of properties that wanted insurance
- The schedule was wrong and incomplete in crucial ways
- Some buildings were missing the year build field making it impossible to tell which construction standard was used
- Other buildings were priced incorrectly leaving a gap for a lawsuit if there is a claim
- The contents of the schedule were used to model each variable.
- Where Sue predicted a different value, the properties were inspected.
- Where years were missing, Sue predicted them.
- None were needed.
- The data in the schedule had hidden relationships in it which DPS was able to extract. Where humans prefer direct cause and effect, DPS is able to look at the total information as a whole and see anomalies
Success and Improvements
- Year built was predicted within 10% of the actual year built on average
- 3 properties flagged for value differences were incorrect
- 10 days of a computer science intern
More Use Cases
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
Sue can approve renewal of risks and bills risks within fuzzy thresholds. Underwriting, Utility Bills
Sue can predict missing data points in a set using the other data.
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
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.
Sue can learn any individual decision in the medical field and replicate it
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
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
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
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
Unlike humans, Sue can review every single item offered and give an initial unbiased opinion Underwriting, Risk Selection, Recruitment Selection, Holiday Authorizations, Exploration Predictions
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.