Category: Persona Based Testing

The ransomware wave wasn’t just a cyber failure—it was a QA gap. Here’s how Crestech’s ERP test automation builds resilience into your business core.

How Crestech Protects Your ERP with QA & Automation

1. ERP Security & Vulnerability Assessment

   • We combine security testing with ERP-specific penetration tests, validating patches and configurations against ransomware-style exploits.

2. Continuous Regression Automation

   • Our ERP test automation frameworks execute regression packs across Finance, Supply Chain, HR, and custom D365 modules—catching breaks before they hit production.

3. Business Process Automation Testing

   • With end-to-end automated test flows, we validate processes like Procure-to-Pay and Order-to-Cash with every release wave, ensuring compliance and continuity.

4. Performance & Load Testing

   • Using automation-driven load simulations, we ensure batch jobs, reconciliations, and month-end closes scale reliably without hidden risks.

5. Human-in-the-Loop + Consulting

   • Where automation ends, our consultants (D365 Finance, SCM, and ERP SMEs) step in to analyze compliance-heavy modules—Revenue Recognition, Landed Cost, Asset Leasing.

👉 With 20+ years of QA leadership, Crestech doesn’t just test ERP—we bulletproof it with a blend of automation, security, and business-first validation.

ERP testing has evolved dramatically over the last decade. With AI-driven frameworks and automation-first approaches, regression cycles that once took weeks can now be executed overnight. In Microsoft Dynamics 365 Finance & Operations, automated packs validate everything from journal postings to supply chain workflows.

Yet, one principle stands firm: ERP testing cannot be left to automation alone.

Why HITL Still Matters

ERP systems like D365 sit at the intersection of technology and business outcomes. A seemingly minor miscalculation can lead to:

Misstated revenue schedules in Subscription Billing.

Compliance breaches in Lease Accounting under IFRS 16/ASC 842.

Supply chain cost distortions due to landed cost misconfigurations.

While automation ensures coverage and consistency, human-in-the-loop (HITL) brings judgment, context, and business validation.

Consider AI-driven recommendations in D365 Copilot:

AI may suggest lowering safety stock based on recent demand dips. On paper, this looks efficient.

A HITL review, however, would recognize upcoming seasonal spikes and prevent a costly stockout.

Crestech’s Balanced Approach

At Crestech, our ERP testing campaigns follow a dual-track model:

1. Automation-first: Automated regression, risk-based prioritization, and synthetic batch monitoring.

2. HITL validation: Finance SMEs, supply chain experts, and QA professionals reviewing high-risk areas where compliance, financial accuracy, or customer trust is at stake.

This combination means our clients don’t just release faster—they release with confidence.

The Bottom Line

In ERP testing, speed is important, but trust is everything.

Automation gives you scale. Human-in-the-loop ensures your ERP decisions are explainable, compliant, and reliable.

At Crestech, we believe trust isn’t automated—it’s tested.

The recent cyberattack that crippled major airports worldwide was more than a headline—it was a wake-up call. It showed how automation, the very engine that keeps critical operations running, can also be the weakest link when not adequately secured and tested.

Airports depend on automated scheduling, baggage handling, and air traffic systems. When these break, flights are grounded, passengers are stranded, and economies feel the impact. The same principle applies to enterprises running on Microsoft Dynamics 365 Finance & Operations (D365 F&O).

Why Automation Is ERP’s Hidden Risk Surface

In D365, automation isn’t a “nice-to-have.” It drives:

• Batch jobs for invoicing, inventory sync, and financial close.
• AI-powered Copilot recommendations for procurement, planning, and forecasting.
• Cross-app workflows across supply chain, commerce, and finance.

But here’s the catch: automation can silently fail. And when it does, damage is often discovered too late. A missed invoice run, a faulty revenue recognition job, or a compromised integration script can erode trust, disrupt cash flow, and expose businesses to compliance risk.

The Crestech QA Lens: Automation Testing Beyond Basics

At Crestech, we treat ERP automation as a mission-critical asset. Our QA approach is designed not just to confirm “it works,” but to ensure it works reliably, securely, and under stress.

Here’s how we do it:

• 🔄 Recovery & Failover Testing – Simulating failures to validate that batch jobs restart predictably and without data loss.
• 📊 Performance Under Stress – Ensuring automation scales with high transaction volumes and peak loads.
• 🔒 Security-First QA – Verifying automated workflows are resilient against tampering, breaches, or malicious triggers.
• 🤖 Copilot Trust Checks – Testing AI-driven automation for accuracy, transparency, and explainability.

The Business Continuity Imperative

Cyberattacks highlight one truth: resilience isn’t built on firewalls alone. It’s built on tested, trusted automation.

For enterprises on D365, QA must evolve from “bug detection” to business continuity assurance. The goal isn’t just to prevent outages—it’s to preserve trust with customers, regulators, and stakeholders.

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Takeaway:

Automation can be your biggest competitive advantage—or your greatest vulnerability. The difference lies in how rigorously you test it.

At Crestech, we make sure D365 automation is not just efficient, but resilient. Because in today’s world, when automation fails, business stops.

 

In September 2025, Jaguar Land Rover (JLR) faced a massive business disruption when its ERP system went down, halting production and impacting services across regions. According to BBC News, the outage left dealers unable to place orders and disrupted critical supply chain and customer processes.

The impact wasn’t just operational—it cascaded to customers, partners, and the brand’s credibility. And the root cause? A system issue that could likely have been prevented with more resilient testing and quality controls.

The Hidden Risk in ERP Systems

ERP platforms like Microsoft Dynamics 365 Finance & Operations (F&O) are at the heart of modern enterprises. They handle:

• Revenue recognition & compliance
• Supply chain orchestration
• Customer and partner transactions
• Critical reporting & analytics

A small loophole—whether in batch jobs, revenue schedules, or supply chain configurations—can ripple into multi-million-dollar consequences.

What the JLR Incident Teaches Us

The JLR case is a textbook reminder that ERP testing is not about bug-hunting; it’s about business risk management. Without robust QA practices, organizations expose themselves to:

• Service halts that paralyze operations
• Financial misstatements that draw regulatory scrutiny
• Brand damage that takes years to repair
• How QA Protects Business Outcomes

 

At Crestech, we specialize in QA for Dynamics 365 F&O, ensuring that ERP deployments and upgrades withstand real-world stress. Our approach combines:

• Risk-based testing → Prioritizing compliance, revenue, and supply chain flows
• Scenario simulation → Stress-testing business exceptions, failovers, and batch jobs
• Domain alignment → Embedding finance, supply chain, and regulatory SMEs into the QA cycle
• Automation & monitoring → Continuous validation of critical ERP modules

The result? ERP systems that are resilient, compliant, and trustworthy.

Final Word

The JLR outage shows us that quality isn’t optional—it’s existential. In an interconnected world, ERP downtime doesn’t just stop transactions; it stalls the business ecosystem around it.

With the right QA strategy, these risks are avoidable. At Crestech, our mission is to ensure that your Microsoft Dynamics 365 journey is future-proof, resilient, and business-aligned—so your ERP never makes the headlines for the wrong reasons.

Batch jobs are ERP’s heartbeat.

• They handle everything: inventory sync, invoicing, financial close.
• But most teams only notice batch jobs when they fail—by then, damage is already done.

Why Batch QA Matters

•  Silent Failures: A missed invoice run or sync failure can cost millions.
•  Scaling Challenges: As data grows, job performance degrades.
•  Recovery Testing: Few QA teams test what happens after failure.

A Real-World Story

We discovered a scheduling error that caused order processing delays for a global retailer. Left unchecked, it would have caused stockouts and missed SLAs.

We now build synthetic monitoring + failover simulations for every client’s critical jobs.

The Takeaway

1. Proactive QA transforms batch jobs from a liability to a competitive advantage:
2. Predictable performance.
3. Reduced downtime risk.
4. Confidence in automation scaling.

💬 When was the last time you tested your automation recovery scenarios?

“The dashboard says revenue is up.”

But is it?

We’ve seen too many organizations blindly trust their ERP dashboards, only to uncover mapping errors, reconciliation gaps, or compliance violations months later. In a world where numbers drive boardroom decisions, QA must be your truth filter.

Why Reporting QA Matters

Multiple Data Layers: Source tables, ETL processes, BI dashboards—each adds risk.

Compliance Pressure: Regulatory filings depend on accurate ERP data.

Decision Impact: CFOs and CIOs make million-dollar calls based on these numbers.

A Real-World Story

We found a $5M discrepancy in quarterly reporting. The root cause? A single mapping error in the ETL layer. Without QA intervention, this error would have gone straight to auditors.

By validating every step—from transaction data to dashboard visuals—we protected the client’s credibility.

The Takeaway

QA in reporting isn’t about pretty visuals; it’s about trustworthy insights:

Cross-system data reconciliation.

Performance and load testing for executive dashboards.

Compliance validation for every metric.

 

 💬 How confident are you in your dashboards today?

AI isn’t “new” to ERP, but it’s everywhere now. Microsoft Dynamics 365 Copilot is suggesting forecasts, drafting emails, and automating decisions that once required human oversight.

 

That’s powerful—but also dangerous if untested.

Why AI in ERP Changes QA

Traditional testing focuses on fixed workflows. AI breaks that paradigm:

AI Learns and Changes: Outputs evolve, which means QA is continuous, not static.

Bias and Transparency Issues: AI decisions aren’t always explainable, but auditors still want answers.

Business Stakes Are Higher: An incorrect AI-driven forecast can derail procurement or overstate revenue.

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A Real-World Example

In one project, Copilot suggested reducing safety stock based on recent demand dips. Looked fine in isolation.

But QA dug deeper: The data model had underweighted a seasonal spike.

Had this gone live, the company would have stocked out during peak season.

 

We solved it by building AI validation frameworks: automated data checks + human-in-the-loop reviews for high-risk actions.

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 The Takeaway

Testing AI isn’t about breaking models. It’s about testing trust:

Explainable outputs.

Scenario validation under edge conditions.

A balance of automation + expert review.

AI can’t replace ERP expertise—it amplifies it. QA ensures it does so safely.

 

💬 Would you trust AI to make financial decisions in your ERP today?

ERP testing isn’t just about finding bugs; it’s about protecting business outcomes.

Modules like Subscription Billing, Landed Cost, and Asset Leasing hold the keys to financial accuracy and compliance. And in many cases, a “small” testing miss can spiral into audit findings, margin erosion, or leadership mistrust.

Why These Modules Are Different

Unlike standard ERP workflows, these modules handle:

Revenue Recognition Rules: A single subscription term change can throw off deferred revenue schedules.

Global Supply Chain Costs: Landed cost calculations require precision across duties, freight, and multi-leg shipments.

Lease Accounting Compliance: IFRS 16 and ASC 842 compliance make Asset Leasing not just an ERP feature, but a regulatory requirement.

This isn’t a “nice to test” situation—it’s mission-critical QA.

A Real-World Lesson

We worked with a global SaaS client migrating to D365’s Subscription Billing module. During testing, we uncovered a scenario where mid-cycle plan upgrades didn’t recalculate revenue schedules properly. On paper, this looked like a “timing difference.”

In reality, it would have misstated revenue by millions and triggered an audit investigation.

That issue was caught before go-live. Why? Because we test ERP the way CFOs, auditors, and controllers think, not just the way a requirements doc reads.

 The Takeaway

Complex ERP modules demand a business-first QA mindset:

Risk-based testing, prioritizing compliance and financial accuracy.

Scenario coverage that simulates real-world exceptions.

Collaboration between QA, finance SMEs, and supply chain leaders.

ERP QA done right is invisible—and that’s the point. No fire drills, no rework, just trust.

💬 What’s the hardest module your team has had to test?

 

Financial services are facing a major challenge. Fraudsters are getting smarter, and traditional tools are no longer enough. This is where predictive analytics is making a difference.

What is Predictive Analytics?

Predictive analytics is a method that uses historical data to make informed guesses about future outcomes. It involves statistics, machine learning, and data mining techniques. The goal is to identify patterns in data that can predict events like fraud or loan defaults.

The Rise of Fraud in Financial Services

Digital transactions surged during COVID-19. Along with it, fraud cases increased rapidly. Hackers exploited system vulnerabilities. Financial services were especially at risk. They serve large customer bases who are often unprotected. Fraud not only causes financial loss but also breaks customer trust. Once that happens, it’s difficult to rebuild. That’s why many financial service providers are turning to AI and predictive analytics software.

Why Predictive Analytics Matters in Financial Services

Financial services rely on trust and volume. A small increase in fraud can cause massive damage. Predictive analytics allows these organizations to detect suspicious behavior early. That means fewer losses and faster responses. For example, if a borrower’s repayment pattern suddenly changes, the system can raise a red flag. The institution can intervene before the loan defaults. This saves time, money, and relationships.

Real-Time Detection and Prevention

Modern predictive analytics software tools can analyze huge datasets instantly. They use real-time screening to check for anomalies. If something unusual happens—like multiple accounts linked to one IP address—it is flagged immediately. These tools also use network analysis. That means they look at relationships between people and transactions. If multiple users are connected to one source or location, the software can detect it.

Want to learn how we test these systems for accuracy and performance?

Check out our fintech testing solutions.

Advanced Features in Predictive Analytics Software

The latest tools do more than just scan data. They also offer:
• Biometric authentication for added security
• Natural language processing to analyze written data
• Integration with blockchain to improve transparency
With these layers in place, financial services can stay ahead of fraudsters.

Predictive Analytics in Risk Management

Risk is unavoidable in financial services. But poor risk management can destroy a firm. Predictive analytics helps organizations identify risky loans before they become a problem. The process starts with collecting borrower data. This includes repayment history, income levels, and behavioral data. The system then uses machine learning to score risk levels. Loans with high default chances are flagged. These models get better over time. As more data is added, predictions become more accurate. This is where predictive analytics software tools show their full value.

Case Study: Tala

Tala is a fintech company that offers microloans in developing countries. Many of their customers don’t have credit scores. Instead, Tala uses behavioral data from smartphones to evaluate loan applicants. Their AI models analyze how often people call their contacts, pay bills, or recharge their phones. Using predictive analytics, Tala can offer loans in minutes—with low default rates.

Benefits for Financial Services

By adopting predictive analytics, financial services can:
• Reduce fraud
• Improve loan repayment rates
• Lower operational costs
• Offer better services to their customers
These benefits are essential for long-term growth.

Predictive Analytics Software: What to Look For

If you are considering adopting predictive analytics software, here are a few things to consider:

1. Scalability – Can the tool grow with your business?
2. Speed – How quickly can it analyze new data?
3. Accuracy – What’s the success rate in detecting fraud or predicting defaults?
4. Integration – Can it work with your existing systems?
5. Compliance – Does it meet regulatory standards?
   It’s also important to choose a tool that your team can use easily. A complex dashboard might slow down decision-making.

Future Trends in Predictive Analytics for Finance

The demand for AI-based fraud detection is growing. Forecasts suggest that the global market for these tools will grow at a compound annual rate of 19.1% between 2023 and 2028. New trends include:
• Real-time fraud detection in milliseconds
• Improved explainability in AI models
• AI-driven early warning systems
• More secure biometric tools
These advances will help financial services not just survive but thrive.

Challenges and Limitations

Despite its benefits, predictive analytics has some challenges. One issue is data quality. Inconsistent or incomplete data can reduce accuracy. Another challenge is the lack of trained professionals. Using these tools requires expertise in AI, statistics, and finance. Many organizations in developing regions still struggle with this. Lastly, some predictive models may inherit bias from historical data. It is critical to audit these models regularly to maintain fairness and transparency.

Conclusion

Predictive analytics is reshaping how financial services manage fraud and risk. With the right predictive analytics software, organizations can detect fraud early, improve risk management, and serve their communities better. By using AI and data-driven tools, financial services can become more resilient. This helps them achieve their mission of financial inclusion in a secure and sustainable way.

In an era where software quality directly impacts brand trust, market share, and user
retention, relying solely on traditional testing methodologies is no longer viable. Modern
software teams, particularly those in high-stakes industries like insurance, need flexible,
scalable approaches to testing. Enters the Hybrid Model in Software Testing.
This blog explores the hybrid model in manual testing, its frameworks, real-world
examples, and why it is rapidly becoming the backbone of resilient QA strategies.

What Is Hybrid Model Testing?

Hybrid Model Testing refers to a testing approach that combines multiple testing
methodologies—typically manual and automated testing—to leverage the strengths of
both. Instead of relying solely on human intervention or machine execution, hybrid testing
creates a balanced ecosystem where different testing strategies coexist.
This model is particularly useful in complex enterprise applications where some
components require the precision of manual testing (like exploratory or usability testing),
while others benefit from the speed and repeatability of automation (like regression or
performance testing).

Why the Hybrid Model in Manual Testing Is Gaining Ground

Manual testing is invaluable when human intuition, observation, and creativity are
required. However, it becomes inefficient at scale. This is where the hybrid model becomes
crucial—it doesn’t discard manual testing; instead, it enhances it.
For instance, in regulated domains such as insurance software testing, where workflows
must meet strict compliance guidelines, manual testing validates nuanced scenarios,
while automation ensures quick feedback loops for unchanged features.

Benefits of the Hybrid Model in Software Testing

1. Balanced Risk Coverage
Hybrid testing helps you cover both exploratory and regression test cases, ensuring
critical paths and edge cases are tested effectively.
2. Faster Time to Market
Automation accelerates repetitive tasks, while manual testing focuses on complex
or new features—resulting in faster and more thorough release cycles.
3. Cost Efficiency
While automation may require upfront investment, hybrid testing ensures ROI by
reducing manual efforts over time without compromising test depth.
4. Adaptability Across SDLC Phases
Whether you’re in development, staging, or post-release monitoring, hybrid testing
provides adaptable strategies for each phase.

Hybrid Testing Examples

• Case 1: Insurance Claims Portal
A manual tester verifies edge-case scenarios like multi-claim dependencies or
exception handling, while an automated test suite covers common use cases like
form submissions or policy number validations.
• Case 2: Healthcare Management System
Automated scripts perform load testing on appointment scheduling APIs, whereas
manual testing ensures patient-facing UIs are user-friendly and accessible.
• Case 3: E-commerce Checkout Flow
The payment gateway is tested manually for UI issues and error handling, while
regression tests for cart, inventory, and order summary are automated.

The Structure of a Hybrid Testing Framework

A well-defined hybrid testing framework combines elements of keyword-driven, data
driven, and modular testing frameworks, layered with a test management platform. This
allows QA teams to:
• Reuse modular components for automated scripts
• Execute manual test cases in tandem with automation runs
• Integrate with CI/CD pipelines
• Map test coverage to business risk priorities
Popular tools like Selenium, TestNG, and Appium can be part of the automation suite, while
tools like TestRail or Zephyr manage manual test cases and results.

Key Considerations Before Adopting a Hybrid Model

• Team Skill Set: Ensure your QA team is skilled in both manual and automation
disciplines.
• Tool Integration: Choose tools that support interoperability across manual and
automated testing efforts.
• Test Case Selection: Not all tests should be automated—identify repetitive and
stable test scenarios to prioritize automation.

Final Thoughts

Hybrid Model Testing offers a pragmatic, scalable solution for teams balancing speed,
accuracy, and cost. It reflects the reality that no one-size-fits-all approach works in
software testing—especially in sectors like insurance, where precision and performance
are non-negotiable.
By blending the human insight of manual testing with the efficiency of automation, QA
teams can confidently deliver quality software that meets both business goals and user
expectations.