Start: Tell Us Where You Stand Right Now

• In one sentence, how would you describe the current state of your fraud operations and dispute handling?
• Which best describes your monthly transaction volume right now? Options: Under 100k, 100k–500k, 500k–2M, 2M–10M, Over 10M
• Over the last 12 months, how have your fraud losses trended? Options: Up sharply (>25%), Up moderately (10–25%), Stable (±10%), Down moderately (10–25%), Down sharply (>25%)
• Which channels are driving most of the fraud you see today? Options: Card-not-present / e-commerce, Card-present / POS, ACH / bank transfers, Account takeover (logins), New account / onboarding, Other
• Who is the primary owner of day-to-day fraud decisioning in your org (title/team)?
• What does your current detection stack look like (select all that apply)? Options: Rule-based engine, In-house ML models, Third-party ML vendor, Hybrid rules + ML, Manual analyst triage only, Other

What Keeps You Up at Night About Fraud?

• How much longer are you willing to tolerate a setup where fraud losses rise while legitimate transactions get blocked? Options: Not at all — urgent, 3 months, 6 months, 12+ months, We haven't decided
• What specific examples of customer experience pain have you seen because of false positives (stories, complaint themes, escalations)?
• Quantify your current false-positive rate or range (authorization declines that were later deemed legitimate). Options: <0.5%, 0.5–1%, 1–3%, 3–5%, >5%, Unknown
• How are rising account-takeover incidents affecting your regulatory exposure or your readiness for a possible exam? Options: Immediate concern — likely exam, Elevated risk, Monitoring but not urgent, No regulatory concern currently, Unsure
• How do fraud losses and customer complaints translate into measurable business impact for you (revenue loss, churn, operational cost)? Please give approximate numbers or examples.
• Which of these pain points feels the most urgent to fix right now? Options: Reduce fraud losses, Reduce false positives / CX complaints, Improve dispute timelines / Reg E compliance, Lower operational dispute cost, Improve detection of account takeover, Other

Who Holds the Keys — Decision Roles and Timelines

• If this initiative fails, whose performance or team outcomes would be most at risk?
• Which stakeholders should be involved in approval conversations for a new detection + dispute platform (select all that apply)? Options: Head of Fraud/Payments, VP Customer Experience, CRO/Head of Revenue, Chief Risk Officer, Compliance/Legal, IT/Engineering, Card Processor/Payments Ops, Procurement, Other
• What is your typical procurement and decision timeline for this class of platform? Options: Under 2 months, 2–4 months, 4–8 months, 8–12 months, Depends on exec buy-in
• What single criterion will make the exec committee say 'yes' (e.g., X% fraud reduction, Y% fewer false positives, $ savings)?
• Who is the regulatory or compliance owner we should align with on Reg E/Reg Z timelines and responsibilities?
• How do you prefer to structure vendor governance during evaluation: weekly steering, monthly checkpoints, or ad-hoc working sessions? Options: Weekly steering, Bi-weekly working sessions, Monthly checkpoints, Ad-hoc meetings

Show Us the Data — What You Have and What You Don't

• If you could hand us only one dataset today to prove value, what would it be and why?
• Which of these data elements can you provide historically for at least 12 months? Options: Authorization logs (auth result, merchant, BIN), Full transaction payload (amount, MCC, IP), Device/browser signals, Account login/auth history, Chargeback / dispute case records, Customer contact & profile, No historical labeled data
• How quickly could you provide a sanitized sample dataset for an initial benchmark (from approval to delivery)? Options: Under 1 week, 1–2 weeks, 2–4 weeks, Over 4 weeks, Unknown
• How is data shared today for vendor integrations? Options: SFTP batch files, Near-real-time API, Message queue (Kafka/RabbitMQ), Database access, Manual file drops, Other
• Are there specific privacy, residency, or tokenization constraints we need to know (PII removal, EU residency, processor masking)? Options: PII removed/anonymized, Requires tokenization, EU/UK data residency, No special constraints, Other
• Who will be our technical data contact to unblock schema questions and samples?

If This Worked — What Would It Change for You?

• What measurable outcomes would make you say this engagement was a success at 60 days, 6 months, and 12 months?
• Which of these KPIs matter most to your team (choose up to three)? Options: Fraud loss % reduction, False-positive rate, Chargeback volume, Time-to-provisional-credit (Reg E), Per-case operational cost, Model adaptation speed to new fraud patterns
• What is the minimum fraud-loss reduction you’d accept to justify moving forward (range)? Options: <10%, 10–20%, 20–30%, 30–40%, >40%
• How much false-positive increase (if any) would you tolerate temporarily if fraud detection materially improved? Options: None — zero tolerance, Up to 0.5% increase, 0.5–1% increase, 1–2% increase, Depends on customer impact
• Beyond metrics, what would operational or cultural changes would indicate success (e.g., analyst trust, fewer escalations, faster investigations)?
• Which stakeholders need to see a specific dashboard or report to feel confident in cutover? Options: Fraud Ops, Compliance, Customer Experience, CRO/Execs, Finance, IT/Platform

What Could Break This — Let’s Surface Risks Early

• What internal objections have historically killed projects like this in your organization?
• Which technical or integration blockers concern you most (select all that apply)? Options: Processor integration complexity, Data latency/quality, Legacy stack constraints, Lack of sandbox/test environments, Insufficient engineering bandwidth, Security/compliance gating
• How concerned are you about model performance drifting when your fraud patterns change? Options: Very concerned, Somewhat concerned, Neutral, Not concerned
• If dispute automation makes an error affecting Reg E timelines, what is your preferred remediation and accountability approach? Options: Vendor pays remediation costs, Joint root-cause + fix plan, We retain control of certain Reg E steps, Other
• Describe any organizational constraints (headcount, analyst bandwidth, union rules, or outsourcing limits) that could slow adoption of automated workflows.
• What rollback or safety mechanisms would you require during a parallel run or cutover? Options: Parallel scoring with dual-decision gating, Manual override for flagged cases, Phased merchant-by-merchant cutover, Fixed performance acceptance thresholds, Other

Agreeing What a 60-Day Parallel Test Must Prove

• What are the unmissable success criteria for the 60-day parallel scoring test?
• Which metrics will you require in the parallel test report (choose all that apply)? Options: Detection lift by fraud type, False-positive rate by channel, Dispute resolution timeline comparisons, Per-case ops cost delta, Model adaptation speed to new patterns, Reg E compliance tracking
• What sample size or proportion of traffic would you be comfortable running in parallel to validate results? Options: 10% of traffic, 25% of traffic, 50% of traffic, 100% (shadow scoring only)
• Who will own acceptance approval at the end of the parallel test (title/team)?
• If the test meets all technical KPIs but internal stakeholders disagree, what escalation path should we follow? Options: Executive steering review, Independent audit of metrics, Extend parallel test, Other
• Are there specific merchants, BIN ranges, or customer segments you want excluded or prioritized during the test?

If We Move Forward — Practical Next Steps and Owners

• What single thing would make it easiest for your team to approve a pilot today?
• Who should be on a joint working team to execute the benchmark and parallel test (names/titles if possible)?
• What is your target date to start a benchmark run and the subsequent 60-day parallel test? Options: Within 2 weeks, 2–4 weeks, 1–2 months, 3+ months, TBD
• Which of the following would remove friction from contracting and data access? Options: A short pilot contract, POC SOW with limited scope, Pre-signed NDAs, Standard security questionnaire already on file, None of the above
• What communications or training will your operations team need before we begin parallel scoring and dispute automation? Options: Hands-on analyst workshops, Playbooks & SOPs, Living dashboard access, Monthly change reviews, Minimal — we'll adapt
• Anything else you want us to know that would help tailor the pilot to your realities (cultural, technical, or political)?

Paint Me Your Fraud Picture

• How would you briefly describe your current fraud detection setup? Options: Strict rules-only engine, Hybrid rules + ML, Proprietary ML models, Third-party ML vendor, Manual review heavy, Other
• Which systems or tools are actively scoring authorizations and routing cases today? (select all that apply) Options: In-house scoring engine, Card network rules, Processor-provided rules, SIEM/APM monitoring, Third-party ML vendor, Case management / RMS, Other
• What is your typical monthly transaction volume that must be scored in real-time? Options: <100K, 100K–500K, 500K–2M, 2M–10M, >10M
• Over the last 12 months, what headline metric best describes the trend you’re seeing? Options: Fraud loss increasing rapidly, Fraud loss stable, False positives increasing, Chargebacks increasing, Dispute backlog growing, Other
• Which fraud types dominate your losses today? (pick top 3) Options: Account takeover (ATO), Card-not-present / eCommerce, Synthetic identity, Friendly / first‑party fraud, Refund / return abuse, Merchant collusion, Other
• Tell us about a recent fraud incident that felt especially disruptive — what happened and why it mattered?

Where It Actually Hurts

• Which single operational friction silently eats margin or reputation faster than you admit? Options: High chargeback costs, Manual dispute overhead, False-positive customer churn, Regulatory remediation risk, Investigation backlogs, Other
• How have daily operational costs changed in the last 12 months because of fraud or disputes? Options: Decreased, No meaningful change, Increased 1–10%, Increased 10–50%, Increased >50%
• Which teams bear the brunt of the extra work (choose all that get pulled into fraud/disputes) Options: Fraud investigations, Customer service/ops, Legal/compliance, Payments/processor ops, IT/SRE, Merchant relations, Other
• Can you quantify the monthly average docket of new dispute cases and the backlog (open cases)? Options: <100 new / <100 backlog, 100–500 new / 100–500 backlog, 500–2,000 new / 500–2,000 backlog, >2,000 new / >2,000 backlog
• Share a concrete example of how a false positive or missed fraud case affected a customer relationship or regulator conversation.

Are We Chasing Ghosts or Catching Real Fraud?

• How confident are you that the majority of alerts your system generates are true positives? Options: Very confident (>80% true), Somewhat confident (60–80%), Uncertain (40–60%), Not confident (<40%)
• What is your current false-positive (FP) rate at the authorization and investigation stages respectively? Options: <1%, 1–3%, 3–7%, 7–15%, >15%
• Where do false positives most often occur — specific channels, products, or customer segments? Options: Mobile app, Web checkout, Call center, New-to-bank customers, High-risk merchant categories, Card-not-present only, Other
• How do you currently validate that a blocked transaction was legitimate — and how long does that validation typically take? Options: Automated device fingerprinting, Manual customer callback, Merchant dispute response, Post-authorization review, We often cannot validate, Other
• Describe a recent pattern where you realized thresholds or rules were causing collateral damage — what changed and what did you do?

Follow the Paper Trail: Disputes, Workflows, and Reg E

• If a regulated dispute arrives tomorrow, do you trust your timeline records and notifications to withstand regulatory review? Options: Yes — full confidence, Mostly, with minor gaps, No — several gaps, Unsure
• How automated is your dispute lifecycle today (from customer claim to resolution)? Options: Fully manual, Hybrid manual + basic automation, Mostly automated with human review, Fully automated, exception-only human review
• What percent of disputes meet Reg E provisional-credit and notification deadlines on time? Options: >95%, 80–95%, 60–80%, <60%
• Which systems record your dispute milestones and generate customer communications? Options: Core banking ledger, Case management system, Custom spreadsheets, Third-party dispute automation, Processor tools, Other
• Tell us about a dispute that escalated to a regulator or litigation — what broke down operationally or procedurally?

Who Really Pulls the Trigger?

• When an alert is high-severity, whose decision ultimately stops or approves the transaction? Options: Automated policy score only, Investigator/team lead, Fraud ops manager, Dedicated fraud committee, Merchant/processor override, Other
• What are the documented escalation paths for ambiguous cases (and do people follow them)? Options: Strictly followed, Mostly followed, Ad hoc in practice, No formal path
• How many levels of approval exist for manual reversals or provisional credits? Options: None — investigator can act, 1 level (team lead), 2 levels (lead + manager), 3+ levels or committee
• Describe the training and experience profile of staff making final disposition decisions — average tenure and typical certifications.
• What emotional or cultural barriers prevent staff from trusting algorithmic recommendations? Options: Fear of regulatory blame, Customer backlash worry, Lack of explainability, Incentives tied to manual review, Other

Data & Integration Reality Check

• Do you have a single, reliable stream of historical transaction data suitable for model training and backtesting? Options: Yes — centralized and clean, Centralized but requires cleanup, Fragmented across systems, Mostly unavailable or sample-limited
• Which data elements are readily available for every authorization within 100ms? (multi-select) Options: PAN/token, Device fingerprint, IP/geolocation, Merchant category, Customer profile/IDKYC, Previous transaction history, Authorization response codes, Other
• What is your retention window for raw transaction and log data used for model retraining? Options: <30 days, 30–90 days, 90–365 days, >1 year
• How easy is it to provision a secure test dataset that mirrors production for a 60‑day parallel scoring test? Options: Turnkey in days, Requires engineering effort, Lengthy legal/data masking process, Not possible today
• List the top three technical blockers you expect for integration (APIs, latency, tokenization, vendor SLAs, etc.).

If Losses Halved Tomorrow — Let’s Get Specific

• If fraud losses were reduced by 50% in the next quarter, what immediate business metric would you celebrate first? Options: Net loss reduction, Lower dispute processing cost, Improved customer NPS, Fewer regulatory touchpoints, Reduced manual headcount, Other
• What target false-positive rate would be acceptable if it still achieved your fraud-loss goals? Options: <1%, 1–2%, 2–5%, 5–10%, Willing to accept higher FP for loss reduction
• Would you be willing to run a 60-day parallel scoring test where both systems score every transaction? And what would you need to feel comfortable doing so? Options: Yes — ready now, Yes — needs legal/data prep, Maybe — need exec buy-in, No — not possible
• What minimum performance lift (fraud reduction % or $ saved) would justify switching platforms in your view? Options: 10% reduction, 20% reduction, 30% reduction, Specific $ target, Need broader operational improvements
• What are the top risks that would stop you from approving a cutover after a successful parallel test? Options: Model drift concerns, Integration instability, Staff resistance, Regulatory/compliance gaps, Unexpected customer impact, Other
• What would a realistic timeline look like from pilot kickoff to production cutover if stakeholders prioritized this project? Options: <3 months, 3–6 months, 6–9 months, >9 months

Quick Grounding: One-Minute Snapshot

• In one sentence, what is the single, most urgent fraud problem you want solved right now?
• Which of these best describes your monthly payment volume? Options: <100k txns, 100k–1M, 1M–10M, 10M–50M, >50M
• Which channels make up the majority of transactions we should focus on? Options: Card-present (POS), Card-not-present (ecommerce), ACH/wire, Mobile wallet, P2P
• Roughly when did you first notice the spike in fraud losses or false positives? Options: Within last 30 days, 30–90 days, 3–6 months, 6–12 months, Longer than a year
• Who will be the primary point of contact for day-to-day work on this engagement?
• Which internal team should receive regular progress updates (select all that apply)? Options: Fraud Operations, Customer Experience/Support, Compliance/Legal, IT/Platform, Risk Analytics, Payments/Processing

When Losses Keep Climbing, What Are You Telling the Board?

• If you had to explain the recent uptick in account-takeover losses to your board in one blunt sentence, what would you say?
• Which of the following root causes do you suspect are driving the rise (pick the top 3)? Options: Credential stuffing/ATO, Synthetic identity, Merchant collusion, Account takeover via social engineering, Weak device signals, Rules tuning drift
• How has the loss increase affected your budget or headcount priorities this quarter? Options: Hiring investigators, Investing in tooling, Increasing chargeback reserves, No change yet, Other
• Which KPIs are your execs watching most closely as evidence we're under control? Options: $ fraud loss, FP rate on approvals, Chargeback ratio, Time-to-resolution (disputes), Customer complaints volume
• How confident are you in the accuracy of the fraud-loss numbers being reported today? Options: Very confident, Mostly confident, Somewhat uncertain, Not confident — we need better data
• Give one concrete example of a recent incident where the reported numbers didn't match what the ops team saw in the casework.

Who Really Decides — and Who Suffers If It Goes Wrong?

• Which individual or role, if not engaged, would stop this project cold? Options: Head of Fraud Ops, CISO/Head of Security, Head of Compliance, VP Customer Experience, COO/Payments Head, Processor Relationship Manager
• What is the target approval or cutover date your leadership expects (or the hard deadline they care about)? Options: Next 30 days, 30–60 days, 60–90 days, Quarter+ (90+ days), No fixed date
• For each key stakeholder, what would “success” look like? Please name stakeholder and their top 1–2 success criteria.
• Who owns regulatory communications if an escalation occurs during the transition (title or team)? Options: Head of Compliance, General Counsel, Risk/AML Lead, Operations Head, Unknown
• What approvals or evidence do you anticipate the legal/regulatory team will require before allowing a cutover? Options: Evidence of parallel-run metrics, Data privacy mappings, SLA drafts, Change control artifacts, Other
• Describe one past project where lack of stakeholder alignment caused delay — what was the root cause?

Are Legitimate Customers Feeling the Pain?

• How frequently are false positives (legitimate transactions blocked) creating customer complaints that reach senior leadership? Options: Daily, Weekly, Monthly, Quarterly, Rarely
• Can you share your measured false-positive rate (authorization declines flagged as fraud) over the past 90 days?
• How many customer contacts or disputes do you see per 10k transactions related to declined authorizations? Options: <1, 1–5, 6–20, 21–50, >50
• Do you have a recent customer story (anonymized) where a false positive caused measurable churn or reputational harm?
• If you could improve one thing about the customer experience during fraud checks, what would it be? Options: Fewer declines, Faster dispute resolution, Clearer communications, Proactive outreach, Other
• Which outcome would your CX team prioritize if forced to choose: lower false positives or higher fraud capture? Options: Lower false positives (CX-first), Higher fraud capture (loss-first), Balanced — target both

If Our Model Misses One New Fraud Pattern, What Breaks?

• Describe the single worst operational or regulatory consequence you fear if a new fraud pattern evades detection for a month.
• How quickly does your current system adapt after a novel fraud pattern appears? Options: Hours, Days, Weeks, Months, We don't adapt well
• What manual monitoring or alarms do you have to detect sudden pattern shifts (select all that apply)? Options: Daily loss dashboards, Rule hit spikes, Chargeback clusters, Customer complaint surge, No automated alarms
• How long does it take to deploy a manual rule change from discovery to production? Options: <1 day, 1–3 days, 4–10 days, 2+ weeks, Longer / unknown
• What's an example of a fraud pattern you only uncovered after significant customer impact — what delayed detection?
• How tolerant are you of initial false negatives during model retraining in exchange for faster adaptation? Options: High tolerance, Moderate, Low, No tolerance

Data: The Good, The Bad, and The Missing

• If I asked for a full day’s worth of enriched transaction events, how clean and complete would that file really be? Options: Ready to ingest (good quality), Minor mapping needed, Significant cleanup required, Partial and inconsistent, We cannot provide
• Which data fields are available today for model training and scoring (select all that apply)? Options: PAN/tokenized PAN, Auth response codes, Device fingerprint, IP/geolocation, Billing/shipping address, Velocity features, Dispute metadata, Customer profile
• How far back does usable historical transaction data go for training models? Options: <30 days, 30–90 days, 3–12 months, 1–3 years, >3 years
• Are there specific privacy, tokenization, or processor constraints that limit data sharing? Options: PCI tokenization only, PII redaction required, Legal approval needed per extract, No constraints, Other
• Can you provide a sanitized sample dataset and schematized field mapping before our integration kickoff? Options: Yes — sandbox ready, Yes — needs anonymization, No — technical/legal blockers, Unsure
• Who owns the canonical transaction feed and who do we contact for test credentials?

Running a 60-Day Parallel Test — What Keeps You Up at Night?

• What single condition would make a 60-day parallel test feel like a wasted exercise to your exec team?
• Which metrics must improve (or at least stay flat) in parallel to consider the test successful (select top 3)? Options: Fraud $ reduction, False-positive rate, Chargeback ratio, Investigation cost per case, Model adaptation speed, Reg E timeline adherence
• What baseline benchmarks or minimum lifts would you require to approve cutover? Options: % fraud reduction required, Max FP increase allowed, Investigation cost reduction target, Regulatory SLA adherence
• Who has final sign-off authority to move from parallel to production? Options: Head of Fraud Ops, CRO/Risk, Head of Compliance, Payments COO, Cross-functional committee
• What safeguards or roll-back triggers do you want in place if parallel behavior deviates after cutover?
• Are you open to a staged cutover (channel-by-channel) instead of a single big switch? If so, which channel should we pilot first? Options: Yes — ecommerce, Yes — POS, Yes — mobile wallet, No — prefer full cutover, Unsure

People and Process: Who Will Use This, and Will They Trust It?

• Which team(s) are most likely to resist an automated dispute workflow if they feel excluded? Options: Fraud investigators, Customer support, Compliance, Vendor ops, IT/Platform
• Walk me through the current investigator workflow from alert to resolution — what are the manual pain points?
• What are your current SLAs for dispute acknowledgment, provisional credit, and final resolution? Options: Same day, 48 hours, 5 business days, 10+ business days, Varies by case
• How large is the team that would use the dispute automation daily (investigators / FTEs)? Options: 1–5, 6–15, 16–30, 30+
• What training format yields the best adoption in your org (select all that apply)? Options: Hands-on workshops, Recorded on-demand, Live Q&A sessions, Train-the-trainer, Documentation + playbooks
• Describe one cultural or process barrier we should anticipate during rollout (e.g., trust in automation, compensation tied to manual reviews).

Integration Reality Check: What's Easy vs. What's Not

• Which single integration (processor, core ledger, or message bus) do you expect will take the longest and why?
• Which of these integrations will be required for the initial parallel test (select all that apply)? Options: Authorization stream, Clearing/settlement feeds, Dispute/case feed, Customer profile API, Merchant onboarding API, Device/IP enrichment API
• Do you have sandbox or test credentials we can use immediately? Options: Yes — fully provisioned, Yes — limited, No — needs request, Unsure
• Estimate how long it typically takes your platform team to grant API access and onboard a vendor: Options: <1 week, 1–2 weeks, 2–4 weeks, 1–2 months, 2+ months
• Who on your technical team owns integrations and what is their availability window for a 3–4 week sprint?
• Are there compliance or PCI scopes we must plan for that will affect integration timing? Options: Yes — major, Yes — minor, No, Unsure

Getting Comfortable — a quick scene-setter

• To make sure we start on the same page: what's the single metric that made you reach out right now? Options: Fraud losses (% YoY increase), False-positive rate, Customer complaints volume, Regulatory concern/inspection risk, Operational costs of disputes, Other
• How many authorization requests do you process per month (ballpark)? Options: <100k, 100k–500k, 500k–2M, 2M–10M, >10M
• Who on your team will feel the most immediate impact if we reduce fraud losses and false positives? Name role(s) and one sentence on why.
• How soon do you need measurable improvement to avoid escalation or regulatory action? Options: Immediately (weeks), Quarterly, 6 months, Longer/unsure
• Is there a recent incident, report, or executive ask we should know about that’s directing this effort? If yes, briefly describe.

Are we flirting with a regulatory examination?

• When you think about regulatory risk today, what’s the most worrying gap—data, timeline tracking, documentation, or response capability? Options: Data availability for audits, Tracking Reg E/Reg Z timelines, Documented dispute procedures, Proof of model governance, Other
• Have you had any recent regulator inquiries, internal audit findings, or external complaints tied to fraud/dispute handling? If so, how recently and what was the outcome?
• How confident are you that your current dispute workflow meets Reg E provisional credit and notification deadlines today? Options: Very confident, Somewhat confident, Not confident, Don’t know
• If a regulator asked for a timeline of an investigation from claim to resolution, what’s the hardest part to produce? Options: Time-stamped events, Ownership trail, Communication templates, Case evidence, Other
• When a regulatory gap is raised internally, who typically leads remediation and how long does it take to get executive sign-off? Options: Head of Fraud Ops, Chief Risk Officer, Compliance, Legal, Cross-functional committee

What’s actually changing in your transaction stream?

• Looking at recent months, what single pattern surprised you most in your fraud data?
• Which fraud types have trended up most (select all that apply)? Options: Account takeover (ATO), Card-not-present (CNP) fraud, Synthetic identity, Friendly fraud/chargeback abuse, Merchant collusion, Other
• How long has this shift been happening? Options: <1 month, 1–3 months, 3–6 months, 6–12 months, >12 months
• Which channels or products are seeing the largest change in fraud — online, mobile, card-present, ACH, or others? Options: Online checkout, Mobile app, Card-present/merchant POS, ACH/wires, Other
• When a new fraud pattern emerges, how quickly does your current system adapt on average (detect and reflect in rules/alerts)? Options: Hours, Days, Weeks, Months, Rarely adapts
• Tell us about the last time a novel fraud tactic bypassed controls — what happened, and what were the downstream impacts?

Who’s being harmed by false positives — and how badly?

• Which customer segment is most frequently affected by false positives (e.g., high-value, new accounts, specific geographies)? Options: High-value customers, New accounts/recently onboarded, Specific region(s), SMB/business accounts, Retail/consumer
• What’s your current false-positive rate on authorization decisions and how is that measured (if you track it)? Options: <0.5%, 0.5–1%, 1–3%, 3–5%, >5%, We don’t have a reliable measure
• Beyond the rate, what are the most tangible harms you see from false positives (revenue loss, churn, CS load, reputational damage)? Options: Revenue loss from declined sales, Customer churn, Increased support volume, Executive escalation/complaints, Brand reputation harm, Other
• How do frontline teams currently handle customer appeals after a legitimate transaction is declined — what’s the emotional and time cost?
• Have you quantified the dollar impact of false positives (e.g., lost transactions, customer LTV impact)? If yes, share the estimate or range.

Walk me through a real case — from alert to dispute

• When an alert is raised for suspected fraud, who receives it first and what are their immediate next steps? Options: Fraud analyst/investigator, Automated workflow/case manager, Customer operations, Third-party vendor, Other
• How many manual touchpoints does an average investigation require today (estimate)? Options: 0–1, 2–3, 4–6, 7+
• Which systems hold the evidence for investigations (logs, transaction history, device data) and how easy is it to assemble a case file? Options: Core ledger, Processor logs, Device/fraud telemetry, CRM/support notes, Other
• Describe how you currently track Reg E or similar timeline requirements inside a case; what’s manual versus automated? Options: Fully manual, Partially automated, Mostly automated, Fully automated, We don’t track reliably
• What’s the single biggest bottleneck in closing a case or preventing repeat fraud?

What would it feel like if this problem were solved?

• If fraud losses dropped by 25–40% and false positives declined meaningfully, what would change in your week-to-week work?
• Which success signals would make leadership consider this project a clear win (select top 3)? Options: % reduction in fraud losses, Drop in false-positive rate, Faster detection/adaptation time, Lower dispute operational cost, Regulatory compliance evidence, Improved NPS/customer complaints
• What is an acceptable tradeoff between fraud reduction and customer friction for you — e.g., is a small rise in manual reviews acceptable to reduce losses? Options: No rise in customer friction, Small rise acceptable, Moderate rise acceptable if losses drop significantly, Unsure — need guidance
• How will you measure model adaptation success when a new fraud campaign begins — what timeframe and metric matter most? Options: Time to detection (hours/days), Recovery in detection rate, False-positive delta, Operational workload change, Other
• Emotionally, what would a successful outcome buy you — peace of mind, career insulation, fewer regulatory headaches, or something else? Options: Peace of mind, Executive trust/recognition, Reduced regulatory exposure, Lower operational stress, Other

Your data and integration truth-telling moment

• Which of the following data feeds can you provide for a 60-day parallel test or model training? Options: Full transaction history (authorization level), Dispute/case outcomes, Device and behavioral signals, Account holder identity data, Processor/settlement logs, We cannot provide all of the above
• What’s the typical latency on transaction feeds to your systems (real-time, near-real-time, batch hourly, daily)? Options: Real-time (<1s), Near-real-time (seconds–minutes), Hourly batch, Daily batch, Other/unknown
• Do you have a separate test environment or sandbox for parallel scoring, and who owns access controls? Options: Yes — dedicated sandbox (owned by IT), Yes — shared test environment, No dedicated test environment, Unsure
• Are there legal, PII, or vendor constraints that typically slow data sharing for pilots? If yes, what are they?
• Which processors, gateways, or core platforms would we need to integrate with for a meaningful test?

How much operational risk are you willing to take in transition?

• What’s the single operational non-negotiable during cutover (e.g., no missed Reg E deadlines, zero customer-visible declines, ability to rollback)? Options: No missed regulatory timelines, No increase in customer-visible declines, Clear rollback process, Full incident response plan, Other
• What cutover triggers would give you confidence to switch to a new system (percent thresholds, time operating in parallel, manual sign-off)? Options: % improvement in fraud loss, Target false-positive rate met, 60-day parallel test success, Operational readiness checklist complete, Executive sign-off
• How fast do you expect the model to adapt when a new fraud pattern appears (hours, days)? Options: Hours, Under 24 hours, 1–7 days, More than a week, Unsure
• Who will be the primary owner for cutover decisions and emergency rollbacks? Options: Head of Fraud Ops, VP Payment Risk, CRO/Head Risk, CTO/Head of Engineering, Other
• If we find the model is over-blocking during parallel, what mitigation path do you prefer — threshold rollback, higher human review, or stepwise rollout? Options: Threshold rollback, Increase manual review, Stepwise regional rollout, Pause and investigate, Other

The 60-day parallel test — what would make it undeniable?

• Why have previous parallel tests failed to persuade executives, if at all? Options: Insufficient sample size, Poorly aligned success criteria, Lack of operational integration, Data quality issues, Other
• Which metrics must move for you to call the parallel test a success (pick top 3)? Options: Fraud-loss $ reduction, False-positive rate, Time-to-detect new patterns, Dispute resolution timelines, Operational cost per case, Customer satisfaction/NPS
• What minimum sample size, or transaction volume, do you consider statistically meaningful for the parallel? Options: <50k, 50k–200k, 200k–1M, >1M, Unsure — need guidance
• How would you like parallel test results presented — daily dashboards, weekly executive summaries, or a combined format? Options: Daily dashboards, Weekly summaries, Bi-weekly deep dives, Final report + interactive dashboard
• If the parallel test shows mixed results across segments, how should we prioritize next steps — fix model, tune thresholds, or adjust operations? Options: Model retrain, Threshold tuning, Operational playbook changes, Segmented rollout, Other

Decision dynamics — who signs off and why

• Who are the decision-makers and influencers for a fraud platform purchase and cutover (roles and influence)?
• What procurement or contracting steps typically take the longest at your institution? Options: Security review, Legal contracting, Procurement approval, Budget committee, Executive sign-off
• Is budget already allocated for this initiative this quarter, or would approval be needed? Options: Budget allocated, Approval needed, Partially allocated, Unsure
• What non-financial concerns often stall deals (e.g., vendor lock-in, model explainability, data residency)? Options: Vendor lock-in, Model explainability, Data residency/compliance, Operational disruption, Other
• If an executive asked for a 90-day playbook to reduce losses and stabilize disputes, what would your one must-have be?

Small bets that de-risk everything — quick experiments

• Would you be open to a focused pilot (e.g., one product line or region) before a full parallel test? Options: Yes — pilot first, Prefer full parallel, Depends on scope, Not at this time
• Which minimal scope would give you confidence quickly — e.g., 30 days on a high-volume channel, or a historical backtest on 3 months of data? Options: 30-day live pilot (one channel), 60-day parallel, Historical backtest, Controlled A/B split
• What internal stakeholders do we need to include in the pilot steering committee? Options: Fraud Ops, Customer Experience/CS, Compliance, IT/Engineering, Risk/Analytics, Legal
• Who should be our primary operational contact to provision data and access for a pilot?
• What would be a realistic next milestone for you after this discovery call (data share, technical kickoff, executive brief)? Options: Share sample data, Technical kickoff, Executive briefing, Procurement initiation, Unsure