Managing Hallucinations & Trust in GenAI: What Building Real Systems Taught Me

One of the earliest lessons I learned working with Generative AI is that correctness is not binary.

Unlike traditional systems, GenAI doesn’t simply fail or succeed. It responds — sometimes confidently — even when it’s wrong. And that behavior fundamentally changes how trust must be designed, not assumed.

Hallucinations are not edge cases. They are a structural characteristic of how these models work.

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The Problem Is Not That Models Hallucinate

At first, hallucinations are often treated as a model quality issue.

Improve the prompt.
Switch the model.
Add more data.

Those steps help, but they don’t eliminate the problem.

The deeper issue is how hallucinations interact with users and workflows. A wrong answer that looks plausible is more dangerous than a visible failure. Once users stop trusting the system, no accuracy metric can bring that trust back.

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Trust Is a Delivery Concern, Not a Model Feature

I’ve seen projects where technically strong models failed in production because trust was never explicitly managed.

Trust is shaped by:

• How confident responses sound

• Whether uncertainty is communicated

• How errors are handled

• What happens when the model doesn’t know

None of these are purely data science problems.
They are design and delivery decisions.

As a Technical Project Manager, ignoring trust means risking adoption — even if the model is statistically strong.

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How Hallucinations Actually Create Cost and Risk

Hallucinations don’t just affect quality. They create downstream consequences:

• Incorrect decisions

• Manual verification work

• Repeated prompts and retries

• Escalations and overrides

• Loss of confidence in AI-assisted workflows

In regulated environments, the impact is even larger:

• Compliance exposure

• Audit challenges

• Reputation damage

Trust issues compound silently until someone decides the system is “not reliable” and stops using it.

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The Shift: From Preventing Hallucinations to Managing Them

At some point, the mindset has to change.

The goal is not zero hallucinations — that’s unrealistic.
The goal is controlled behavior when hallucinations occur.

That means designing systems that:

• Know when confidence is low

• Surface uncertainty instead of hiding it

• Fall back to safer paths

• Involve humans when risk crosses a threshold

This is an architectural choice, not a last-minute fix.

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What Has Worked in Practice

In real projects, trust improved when we focused on a few principles:

• Constraining models to verified sources when accuracy mattered

• Separating creative use cases from factual ones

• Using confidence signals to trigger review

• Designing “I don’t know” as an acceptable outcome

• Measuring user trust, not just model accuracy

Interestingly, users were more forgiving of systems that admitted uncertainty than systems that sounded confident and wrong.

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Why Hallucinations Change the Role of the Project Manager

GenAI projects blur the line between engineering, product, and risk management.

Managing hallucinations means:

• Aligning stakeholders on acceptable risk

• Defining where automation ends

• Setting expectations early

• Making trade-offs explicit

This requires active ownership throughout the lifecycle, not just during delivery.

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A Different Definition of Success

Success in GenAI is not about eliminating errors.

It’s about creating systems that:

• Fail safely

• Protect decision quality

• Preserve user confidence

• Improve over time

Trust is not a feature you add at the end.
It’s something you design from the first architecture discussion.

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Closing Thought

The most dangerous AI systems are not the inaccurate ones.

They are the ones that sound certain when they shouldn’t.

Managing hallucinations is ultimately about managing trust — and trust, once lost, is extremely hard to regain. For me, that has become one of the most important lessons in delivering GenAI responsibly.

AI-Enabled Risk Scoring for TPPs in Open Banking: A Game Changer for Ecosystem Trust

As Open Banking ecosystems mature globally, traditional banks, fintech startups, and regulators face a growing challenge: how to trust the growing number of Third Party Providers (TPPs) that are now gaining access to customer data and payment rails via APIs. In this fast-evolving environment, AI-enabled risk scoring emerges as a powerful tool that can continuously evaluate the trustworthiness of TPPs based on dynamic behavior, financial performance, and regulatory compliance signals.

This article explores what AI-enabled TPP risk scoring is, why it matters, and how it can be practically implemented—both from a strategic and technical lens.

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Why TPP Risk Scoring Is Critical in Open Banking

Open Banking regulations like PSD2 in Europe, OBIE in the UK, and similar frameworks across Africa and Asia mandate that banks open up their customer data (with consent) to licensed TPPs. These TPPs include:

• Payment Initiation Service Providers (PISPs)

• Account Information Service Providers (AISPs)

• Aggregators, budgeting apps, and more

While this enhances competition and innovation, it increases the risk landscape:

• API misuse or abuse

• Poor data handling practices

• Operational failures or security gaps

• TPPs going out of business unexpectedly

Banks must assess “Who are we opening our APIs to?” on an ongoing basis, not just at onboarding. Static checklists aren’t enough.

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Enter AI-Powered Dynamic Risk Scoring

An AI-enabled TPP risk scoring system continuously evaluates TPPs based on:

1. Usage patterns: API request frequency, data volume, error rates.

2. Behavioral anomalies: Sudden surges, unusual endpoints, suspicious transaction types.

3. External signals: Regulatory fines, media sentiment, funding rounds, business health.

4. Peer benchmarking: Comparing a TPP’s activity against industry norms.

5. Historical incident data: Security breaches, customer complaints, etc.

AI models can assign dynamic risk scores that are updated in real time or on scheduled cycles, making it easier for banks to:

• Auto-trigger red flags and throttling

• Prioritize monitoring

• Optimize SLAs or commercial terms

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A Simple Python Example: Behavior-Based TPP Risk Score

Below is a basic Python sketch that uses a decision tree classifier to assess the risk score of a TPP based on its recent API activity profile:

#python
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import pandas as pd
from sklearn.tree import DecisionTreeClassifier

# Sample dataset
data = pd.DataFrame({
    'api_calls_per_day': [1000, 5000, 100, 8000, 300],
    'error_rate': [0.01, 0.15, 0.03, 0.25, 0.02],
    'sudden_spike': [0, 1, 0, 1, 0],
    'past_incidents': [0, 2, 0, 3, 1],
    'risk_label': ['low', 'high', 'low', 'high', 'medium']
})

# Convert labels to numeric
data['risk_label'] = data['risk_label'].map({'low': 0, 'medium': 1, 'high': 2})

# Train model
features = data[['api_calls_per_day', 'error_rate', 'sudden_spike', 'past_incidents']]
labels = data['risk_label']
clf = DecisionTreeClassifier(max_depth=3)
clf.fit(features, labels)

# Predict a new TPP
new_tpp = pd.DataFrame({
    'api_calls_per_day': [4500],
    'error_rate': [0.18],
    'sudden_spike': [1],
    'past_incidents': [1]
})

predicted_risk = clf.predict(new_tpp)[0]
risk_level_map = {0: "Low", 1: "Medium", 2: "High"}
print("Predicted Risk Level:", risk_level_map[predicted_risk])

This is a simplified model. Real implementations might involve:

• Gradient Boosting models or Neural Networks

• Integration with anomaly detection (e.g. Isolation Forests)

• Stream processing for real-time scoring (using Kafka or Spark)

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Challenges and Considerations

Area
Challenge
Recommendation

Data Privacy
Risk of scoring based on sensitive info
Use anonymized or non-PII behavioral features

Fairness
Biased models could unfairly penalize new TPPs
Ensure transparency and use Explainable AI (XAI)

Regulatory Risk
Compliance with GDPR, PSD2, local rules
Document models, consent sources, scoring logic

Interpretability
Business teams need understandable outputs
Use SHAP values or decision paths

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Business Value

• Enhanced Security: Detect rogue or compromised TPPs early

• Regulatory Compliance: Proactive risk posture appreciated by auditors

• Better SLAs: Dynamic risk-based access tiers

• Ecosystem Trust: Encourage responsible innovation by rewarding good actors

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Conclusion

In the Open Banking world, where trust is the currency, AI-enabled TPP risk scoring is no longer optional—it's foundational. Banks and regulators must embrace AI not just for fraud detection, but for proactive partner risk management across the API economy. The path forward involves blending behavioral science, machine learning, and regulatory foresight to make Open Banking safer and smarter for everyone.