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ScienceSoft’s Practices to Ensure Accurate Financial Software Logic

Delivering custom BFSI software since 2005, ScienceSoft knows how to avoid the common pitfalls in financial software logic and applies proven development best practices to enable error-free financial automation. Our approach to building robust software with accurate financial and actuarial logic is based on:

- Accurately translating business rules into software logic.

- Logic-centered, flexible software design.

- Rigorous testing.

- Consistent software documentation.

How to Ensure Accurate Financial Software Logic
How to Ensure Accurate Financial Software Logic

The Cost of Error in the BFSI Industry

The cost of software logic flaws and vulnerabilities in the finance sector is extremely high. For example, in 2023, Deutsche Bank was fined $186M for repeated AML control failures stemming from the erroneously configured compliance checkup parameters. Provident Financial’s share price crashed 66% in a single day as the lender announced that inaccuracies of its new prescriptive system completely disrupted the debt collection process. And Compound Finance lost $80M due to a tiny syntax error in smart contract logic that led to incorrect token distribution.

4 Reasons Behind Poor Financial Software Logic — and ScienceSoft’s Ways Out

What causes errors

How we make it right

Feel the difference with ScienceSoft

Incomplete requirements

Development teams with poor requirement-gathering practices and insufficient BFSI domain knowledge may fail to get the full picture of the financial firm’s needs. Misinterpreting operational requirements for the financial solution and inaccurately translating business rules into software logic rules results in gapped or erroneous logic.

Our experts analyze the workflows that financial software will support and conduct interviews with stakeholders to elicit granular requirements for the solution. We involve subject matter experts to review the requirements and validate the prototyped and coded logic components at later project stages. This way, we can quickly adjust software logic to align with the original business rules.

Mediocre technical design

Poor architectural and tech stack choices can limit the capacity and flexibility of financial software logic and hinder potential integrations. This disrupts data processing flows and leads to flawed execution of business automation rules. Additionally, some tech stack options might introduce security vulnerabilities, exposing the software to cyber threats.

Our architects consider logic scope and complexity as well as case-specific software accuracy requirements to pick the optimal architectural patterns and programming techs. We prioritize flexible modular architectures as a way to introduce easy-to-evolve software with reusable logic. Our teams never use obsolete components and practices that may put software precision, integrity, and security at risk.

Poor code quality

Developers’ limited tech skills, subpar coding practices, over-engineering, and resistance to documentation may result in erroneous, vulnerable, or overly complex financial software code. Convoluted code complicates testing and increases the risk of overlooking logic flaws. Plus, complex code is hard to modify and debug, which may result in severe logic disruptions every time new features are added.

ScienceSoft’s developers have a solid background in finance. They adhere to technology-specific code standards to deliver highly readable and testable code. By using dynamic and static code analysis, pair programming, and peer reviews, our teams can spot logic issues quicker and fix them in collaboration. We maintain consistent code documentation to ensure faultless software evolution.

Insufficient testing

Sometimes, development teams may sacrifice testing of financial solution logic when the delivery deadlines are tight. Besides, some companies may involve developers to test their own deliverables, which means a risk of missing errors. One more common cause of undetected or negligently handled logic flaws is over-reliance on test automation when the software needs a more nuanced review from a human tester proficient in BFSI.

ScienceSoft applies an ISO 9001-certified quality management system, which relies on established QA and QC processes and dedicated testing teams to test financial software. Our QA engineers balance manual and automation tests and take a shift-left testing approach to prevent and fix potential logic loopholes and security gaps as early as possible, avoiding costly changes in a live solution.

ScienceSoft’s Head of Business Analysis and Banking IT Consultant

Bridging logic accuracy and compliance

100% accuracy of financial software doesn’t necessarily mean 100% regulatory compliance. Developers may care for utmost logic precision but overlook critical legal requirements, which results in non-compliance. Also, accuracy does not always assume transparency. If software logic is opaque and hard to interpret, it may be hard or even impossible for a finance company to prove its compliant and ethical practices to regulators.

To ensure financial software compliance from the onset, our consultants elicit complete regulatory requirements for the solution early on. Along with business rules, we translate them into specific logic requirements guiding the solution’s functional design. Addressing the need for clear and impartial automation logic, we create fully explainable algorithms that provide transparent reasoning for each automatic output. In our projects, we help make financial solutions compliant with sectoral data protection standards (PCI DSS, GLBA, GDRP, NYDFS, etc.) and frameworks like AML/CFT, SEC, SOX, ECOA, FDCPA, and NAIC.

Logic Faults That Never Occur in Our Custom Financial Solutions

ScienceSoft’s approach to financial software accuracy guarantees that the solution functions as intended, adheres to the necessary legal frameworks, and is protected against exploits.

Financial calculation errors

  • Incorrect financial, actuarial, and accounting formulas.
  • Erroneous application of financial service fees.
  • Errors in tax computations.
  • Company and customer balance discrepancies due to rounding errors.
  • Mishandling currency conversion fees.

Financial transaction processing errors

  • Duplicate payment transaction processing.
  • Misdirected funds transfers.
  • Misallocation of received funds.
  • Incorrect transaction sequencing.
  • Transaction rejections due to integration failures.
  • Transactional data corruption or loss.

Financial data and document handling errors

  • Accepting invalid or expired customer documents.
  • Errors in generating financial service agreements, policies, and invoices.
  • Duplicate, missing, or inaccurate data entries.
  • Errors in converting data and documents to a different format.
  • Erroneous reconciliation of data from corporate and external systems.
  • Use of outdated or incorrect financial market data feeds.
  • Repeated data entry mistakes due to poorly designed forms.

Financial operation logic errors

  • Incorrect sequencing of automated financial processes (e.g., enforcing payment before approval).
  • Inaccuracies in automated customer risk profiling.
  • Inaccurate and biased automated loan decisions.
  • Erroneous loan amortization schedules.
  • Misplacement of investment and trading orders.
  • Inaccuracies in automated loss adjustment and claim processing.
  • Excessive false positives and false negatives in financial fraud detection.

Compliance and reporting errors

  • Errors in KYC/AML and OFAC checks for new financial service clients.
  • Software logic is non-compliant with region-specific sectoral regulations.
  • Inaccurate or incomplete regulatory reporting.

Security vulnerabilities

  • Erroneous user authentication and access control logic.
  • Exploitable vulnerabilities in financial software code.
  • Flaws in financial data encryption algorithms.

How We Foster Financial Software Accuracy at Each SDLC Stage

We prioritize accuracy throughout the financial software development lifecycle and beyond. Here are the main steps of our process:


Business analysis and requirements engineering

At this stage, we gather functional and non-functional requirements for the financial solution, laying the basis for accurate logic design.

  • Interviewing project stakeholders and subject matter experts (e.g., financial product specialists, risk analysts, underwriters, financial lawyers) to get a complete vision of the existing business needs and the expected outcomes.
  • Scrutinizing our customer’s unique service operations and data flows to understand the business rules behind them.
  • Analyzing the available financial software documentation.
  • (for software products) Researching the market and target audience needs and defining the trending features to include in the solution.
  • Determining global, region- and domain-specific regulatory compliance requirements.
  • Composing a list of functional requirements for the financial solution.
  • Cross-checking the requirements from various stakeholders and suggesting the optimal ways to address contradictions, if any.
  • Rapid prototyping to represent the high-level logic of particular financial app components.



Financial software design

At ScienceSoft, this stage includes:

  • Business logic design, i.e., mapping the rules, constraints, and processes that will guide the behavior of the financial solution.
  • Creating an optimal feature set for the financial software. This step covers designing specialized logic components for BFSI workflow automation, e.g., custom actuarial formulas, AI models for credit decisioning, image analysis algorithms for financial document processing, blockchain smart contracts for parametric payouts, etc.
  • Determining the business logic components that can be reused across multiple features. Reusable logic contributes to software operational consistency, helps avoid unnecessary development efforts, and enables faster software evolution with minimized risks to the integrity of its underlying business rules.
  • Designing a scalable, secure, and resilient architecture that will enable the smooth execution of financial software logic. We rely on modular SOA and microservices architectures that allow for logic decoupling and separation of concerns, making it easier to reuse, maintain, and evolve logic components.
  • Selecting a reliable financial software tech stack that will support precise calculations, consistent data handling, robust security, and compliance, for example:
    • ACID-compliant databases like Microsoft SQL Server and MongoDB that ensure high data integrity and support complex queries and transactions.
    • Frameworks and libraries specifically designed for financial apps, e.g., QuantLib, Pandas.
    • PCI DSS-compliant cloud services like AWS and Azure.
  • Designing integrations (APIs, custom connectors, etc.) based on the client’s specific data manipulation rules.
  • Designing intuitive UX and UI for role-specific financial apps. To ensure a short learning curve for BFSI professionals and non-finance users, we tailor the tone and complexity of the textual UI components to various user roles (e.g., using field-specific jargon for underwriters and simple terms for insurance clients).

Balancing AI accuracy vs. interpretability

While AI automation is gaining popularity among BFSI market players, proving that your AI outputs are accurate and impartial is a common challenge. There are go-to methods for making AI explainable. However, these assume simplifying complex AI models, which means inevitable accuracy tradeoffs. At ScienceSoft, we carefully balance regulatory requirements for AI logic transparency with the client’s expectations for accuracy. In our BFSI projects, we create hybrid AI models combining complex black-box deep learning algorithms with interpretable ML components. Such models provide a clear rationale behind their outputs and may still offer 95%+ accuracy.

Head of Data Analytics Department at ScienceSoft


Resource planning

Accurately planning a software development project requires:

  • Scoping the development tasks and defining project roles.
  • Arranging the team. We consider our talents’ domain expertise, soft skills, and experience working on similar projects to ensure adequate resource allocation and on-time delivery.
  • Choosing the development methodology that would allow for potential changes (e.g., triggered by early end-user feedback or evolving compliance rules) at later project stages.
  • Identifying accuracy-associated risks (e.g., calculation failures in edge and corner cases, insufficient accuracy of AI models) and ways to mitigate them.



Development and quality assurance

This is the stage where we transform the business logic into financial software code and vet the delivered solution for accuracy and security.

  • Implementing DevOps to automate development, testing, integration, and deployment operations.
  • Setting up a version control environment (e.g., a Git repository) to streamline collaborative code creation, revision, and changing.
  • Coding the solution’s back end and creating user interfaces using secure coding standards like OWASP ASVS.
  • (for AI-powered financial software) Developing explainable AI models that provide the intended accuracy levels while ensuring clear and transparent logical reasoning.
  • (for blockchain-based financial software) Coding business rules in smart contracts and developing proxy contracts to enable on-demand changes in software logic.
  • Processing change requests and implementing feasible software adjustments.

Testing the software in parallel with development (may involve functional testing, code vulnerability assessment, usability testing, and more). Our QA engineers ensure sufficient test coverage for various business scenarios, including non-standard and edge cases, to ensure the solution produces correct results for any operational variables and usage patterns.

Adopting test-driven development (TDD), where tests are written before the corresponding code, helps us naturally improve test coverage, ensuring thorough logic validation and early bug fixing. TDD also encourages our developers to think about software logic and behavior from the end-user perspective, which contributes to a stronger code mental model, app logic clarity, and overall solution usability.

Software Testing Director at ScienceSoft


Integration, pre-launch testing, and deployment

Our activities at this stage are:

  • Establishing financial software integrations with the necessary systems and running integration tests to ensure smooth and secure data flows between the connected solutions.
  • Revising financial software accuracy risks, addressing new risks, and updating the risk mitigation plan.
  • Delivering exhaustive technical documentation and test reports to facilitate financial software audit, maintenance, and evolution.

Pre-launch validation of financial software accuracy (via user acceptance testing) and setting the solution live.

Financial software relies on the company’s proprietary data (e.g., on financial performance, client behaviors) and third-party information (like financial market indices, credit ratings, or insurance risk factors) to inform its operations. The accuracy of data feeds is integral to accurate software functioning. We help our BFSI clients establish a robust data governance framework, and we can power financial software with intelligent data validation mechanisms to ensure the data used by the solution is consistent and reliable.

ScienceSoft’s Financial and Banking IT Consultant


Maintenance and evolution of financial software

Here are some operations we recommend to ensure the accuracy of your financial solution in the long run:

  • Continuous monitoring of the financial software accuracy (e.g., via automated transaction logic checks and data cross-validation).
  • Ongoing software maintenance and support to rapidly handle emerging issues.
  • Regular security and compliance checkups to ensure early response to emerging cyber threats and timely accommodate evolving legal requirements.
  • Continuous regression testing during financial software evolution to ensure that new and changed code pieces do not break logic accuracy.
  • (for AI-powered financial software) Continuously fine-tuning AI model hyperparameters to improve model accuracy and meet evolving regulatory requirements for AI explainability.

Improving accuracy of legacy financial software

A recent report by CISQ estimates that the accumulated technical debt impeding accurate and safe software performance costs US companies roughly $1.52 trillion, which accounts for 64% of all software quality-associated losses.

If the accuracy of your financial solution lags behind its functional objectives, or you doubt the app’s code-level security, ScienceSoft is ready to conduct an independent audit of your financial app, point out the root cause of issues, and advise on the necessary improvements. To enhance the accuracy of your legacy solution, we can strangle its monolith architecture to microservices, review and refactor the code, modernize the tech stack, and build new features to introduce consistent process automation.

Head of Enterprise Application Development

Custom BFSI Software with Comprehensive Financial Logic: ScienceSoft’s Featured Success Stories

Our Customers Say

What stood out was ScienceSoft's proactive suggestions for cost-saving architecture design and tech stack solutions. Their input ensured we stayed within budget without compromising on software quality.

ScienceSoft brought to the table substantial expertise in mobile banking app development and customer-centered app design. We especially appreciate their professional approach to security issues.

ScienceSoft expanded our intranet with custom workflows, automation, and security controls. They completed the work with attention to detail and promptness in providing expert advice.

Get Accurate Software to Power Digital BFSI Workflows

ScienceSoft is ready to provide practical assistance across the entire software development journey – from design to release – to help you get a secure and compliant solution with flawless financial logic.