Technology choices around AI for fraud detection and risk management are easy to reverse on paper and expensive to reverse in practice. Most organisations now hold more data about risk management than they can actually act on. Most organisations already have a policy on the practice within digital and data-driven work; far fewer can show it working. The content is relevant to those who own AI for fraud detection and risk management and to those who are held accountable for its results. It establishes a shared vocabulary for this aspect of digital and data-driven work so that teams can disagree productively about it. Each session closes with a decision the participant must justify about risk management in their own setting. Post-incident reviews keep identifying weaknesses in AI for fraud detection and risk management that were visible long before the incident. They acquire practical criteria for judging when risk management is working and when it is only appearing to. The closing exercise tests whether the participant's plan for the wider digital and data-driven work agenda survives a hostile question.
Expected Learning Outcomes
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Establish version control and rollback for every component of AI for fraud detection and risk management that reaches production.
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Build a security and access model for risk management appropriate to the sensitivity of the data involved.
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Design the pilot for AI for fraud detection and risk management so its result is decisive rather than merely encouraging.
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Assess the current state of risk management against a structured set of criteria rather than impressions.
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Communicate the purpose of AI for fraud detection and risk management to those who have to comply with it.
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Present the case for investment in risk management in terms that a finance function will accept.
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Document the decision record for AI for fraud detection and risk management so successors understand why it is built this way.
Who Should Attend
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Managers of multi-site operations seeking consistency in AI for fraud detection and risk management.
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Product owners prioritising the roadmap for risk management.
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Technology and digital transformation managers leading AI for fraud detection and risk management.
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Quality staff verifying that risk management performs as designed.
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Information security officers reviewing the exposure created by AI for fraud detection and risk management.
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Chief information officers accountable for the investment in risk management.
Course Modules
Open any module to see its sessions and topics.
01
AI for fraud detection and risk management: measuring benefit and retiring what it replaces
2 sessions · 8 points
Session 1Reading the true cost of running AI for fraud detection and risk management
Review whether AI for fraud detection and risk management is aligned with the objectives of the transformation programme.
Close out actions on risk management rather than leaving them open indefinitely.
Confirm the retention and deletion rules applied to data inside AI for fraud detection and risk management.
Decide which legacy process risk management retires, and set the date.
Session 2Building the method for risk management step by step
Verify that AI for fraud detection and risk management still performs when input volume doubles unexpectedly.
Prepare the response for the most likely failure in risk management.
Specify the fallback path when AI for fraud detection and risk management is unavailable.
Agree the indicators that will show whether risk management is improving.
02
Risk management: security, privacy and regulatory obligation
2 sessions · 8 points
Session 1Choosing a supplier for risk management without being captured
Anticipate the objections AI for fraud detection and risk management will raise and prepare the answers.
Design the pilot for risk management so that a negative result is still useful.
Estimate compute and licensing cost for AI for fraud detection and risk management at expected and at peak load.
Agree who is on call for risk management outside working hours.
Session 2What to measure in risk management and what to ignore
Establish the boundary of AI for fraud detection and risk management and record what sits outside it.
Define the trigger that would require risk management to be redesigned.
Identify every system AI for fraud detection and risk management must read from or write to.
Assign responsibility for keeping documentation of risk management current.
03
Risk management: people, skills and the change that follows
2 sessions · 8 points
Session 1Building lasting competence in risk management
Benchmark the organisation's AI for fraud detection and risk management against comparable operations.
Record what was learned when risk management did not go as planned.
Set the metrics that will show whether AI for fraud detection and risk management is drifting from its intended behaviour.
Identify where judgement in risk management is legitimate and where it is not.
Session 2Proving AI for fraud detection and risk management paid for itself
Map the handovers in AI for fraud detection and risk management between functions and secure them.
Name a single owner for each element of risk management.
Record the reasoning behind each architectural choice in AI for fraud detection and risk management.
Plan how risk management is versioned and how a bad release is rolled back.
04
Risk management: architecture, integration and the existing estate
2 sessions · 8 points
Session 1Where risk management touches systems nobody wants to change
Rehearse the briefing on AI for fraud detection and risk management that would follow an incident.
Confirm that those complying with risk management understand why it exists.
Measure the current quality of the data feeding AI for fraud detection and risk management before assuming it is usable.
Assess the regulatory obligations risk management triggers in each jurisdiction.
Session 2What breaks first when risk management meets real volume
Prepare the summary of AI for fraud detection and risk management that senior management will read.
Build the user briefing that explains what risk management does and does not decide.
Test AI for fraud detection and risk management against edge cases drawn from real historical records.
Define the exit route from the supplier supporting risk management.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
Workshop or Program Participation
Airport Transfers
Customized Badge
Expert Mentorship (Private Sessions)
Supervision & Secretarial Services
Accredited Certificate of Participation
Complete Training Kit
Coffee Break
Closing Ceremony
Gold Package
At least 3 people
USD1,850
5-night stay in a 5-star hotel
Workshop or Program Participation
Airport Transfers
Customized Badge
Expert Mentorship (Private Sessions)
Supervision & Secretarial Services
Accredited Certificate of Participation
Complete Training Kit
Coffee Break
Closing Ceremony
Complete your registration
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