Use Case

Auditing

Problem Statement

  • A credit card clearing house has an oversensitive fraud detection algorithm
  • This leads to many false positives on the fraud system
  • Each false alarm requires expensive human intervention – phone calls and verification

DPS Solution

  • Sue was trained to look at the each fraud alert and rate it for the probability that it was real. The client then chooses what probability requires action and which to ignore.

Metrics Gathered

  • Sue gathered all the characteristics of all transactions that have caused alarms before including a mix of both real and false positives.

Success and Improvements

  • Fully 35% of the false positives could be removed without affecting any of the true positives.

Implementation Timeline

  • 2 months to collect the necessary data at the client followed by 2 weeks to develop the model

DPS Similar Possible Use Cases

In any situation where a human can look at a transaction and tell if it is problematic, Sue can look at ALL transactions and test them. Sue moves from sample auditing. Telecoms, Banking, Finance, Triage of Insurance Claims

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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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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

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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

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

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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.