Use Case
Decision Management
Problem Statement
- A bank onboarding process is dynamic and often judgement based.
- Loosely worded regulation means that the bank’s decisions are also based on its own risk appetite.
- As regulations change, regular slow-moving modeling was unable to keep up.
Our Solution
- Sue was embedded within the onboarding document flow at a variety of points.
- Different bodies in the bank trained these decision models based on their jurisdiction.
- Managers from compliance, process and risk each took the necessary decisions through Sue.
Metrics Gathered
- The number of mis-processed applications was measured.
- Changes in the output of risk models was assessed.
Success and Improvements
- Mis-classified applications reduced.
- Precision of application selection improved increasing turnover without adding risk.
- Metrics on fraud models improved incrementally with each new model.
Implementation Timeline
- The average creation and integration of each model was less than 6 weeks.
Similar Possible Use Cases
Any appetite driven decision process including Insurance, Credit Decisions, Pensions, State Benefits…
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
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.
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
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.
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.
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.
Medical
Sue can learn any individual decision in the medical field and replicate it
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
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.
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
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.