Conducting thorough technical due diligence on financial systems is important for financial service organizations. This ensures that the underlying technology supporting financial operations is robust, secure, and capable of meeting the industry's stringent regulatory requirements.
This blog explores some of the common financial systems subject to technical due diligence, highlights key assessment areas, reviews notable public incidents exposing industry risks, and discusses the potential for AI to enhance and optimize financial systems.
Financial Software Solutions
The finance space has a broad portfolio of software solutions, and technical due diligence needs to be capable of assessing these different types of financial software.
Here are some examples of typical finance systems that may be assessed:
Accounting Software
Core financial software that facilitates tasks like general ledger management, accounts payable and receivable, payroll processing, and financial reporting.
Enterprise Resource Planning (ERP) Systems
Integrated software suites covering multiple business functions, including finance, human resources, supply chain, etc. ERP systems provide a comprehensive view of organizational processes.
Financial Planning and Analysis (FP&A) Software
Tools that assist in budgeting, forecasting, and financial analysis. FP&A software helps organizations plan and analyze financial performance to make informed decisions.
Expense Management Software
Solutions that automate the tracking, submission, and approval of business expenses. They ensure compliance with expense policies and streamline the reimbursement process.
Tax Management Software
Software designed to manage tax-related processes, including calculations, compliance with tax regulations, and the generation of accurate tax reports.
Financial Compliance and Risk Management
Systems that assist organizations in adhering to financial regulations and managing risks. These tools often include features for internal controls, audit trails, and risk assessment.
Payment Processing Solutions
Software that facilitates the secure processing of payments, including credit card transactions, electronic funds transfers, and other payment methods. It often integrates with banking systems.
Financial Security and Fraud Prevention
Systems that focus on securing financial data and preventing fraudulent activities. They include features such as encryption, access controls, and monitoring for suspicious activities.
Portfolio Management Systems (For Financial Institutions)
Systems designed for managing investment portfolios, assessing risk, and optimizing investment strategies. They often include features for market analysis, performance tracking, and compliance.
Financial CRM (Customer Relationship Management)
CRM solutions tailored for financial services focus on managing customer relationships, improving customer engagement, and ensuring compliance with data protection regulations.
Banking Software
Comprehensive software solutions designed specifically for banking institutions. This includes core banking systems, online banking platforms, mobile banking applications, and anti-money laundering (AML) software.
Insurance Software
Software tailored for the insurance industry, including policy management systems, claims processing software, underwriting solutions, and insurance analytics tools.
Wealth Management Software
Solutions that assist financial advisors and wealth managers in managing client portfolios, providing financial planning services, and facilitating investment management.
Financial Trading Platforms
Software used in financial markets for executing and managing trades. This includes electronic trading platforms, algorithmic trading systems, and trading analytics tools.
Financial Software: Technical Due Diligence
Data Integrity, Transaction Processing, And System Scalability Risks Are Meticulously
Examined To Safeguard Against Potential Disruptions
Technical due diligence is a proactive measure that aims to uncover potential risks and opportunities inherent in the financial system's infrastructure, providing stakeholders with a comprehensive understanding of the technology's current state and future potential.
One crucial aspect of technical due diligence is identifying and mitigating various risks associated with financial systems. This includes assessing the security measures to protect sensitive financial data, ensuring compliance with industry-specific regulations, and evaluating the system's resilience against potential cyber threats. Data integrity, transaction processing, integration and system scalability risks are meticulously examined to safeguard against potential disruptions with severe financial implications. Additionally, technical due diligence scrutinizes the system's ability to adapt to evolving regulatory landscapes, ensuring ongoing compliance with changing financial standards.
Simultaneously, technical due diligence unveils opportunities for optimization and enhancement within financial systems. This examination delves into the efficiency of transaction processing, identifies areas for performance improvement, and assesses the potential for leveraging emerging technologies such as blockchain or artificial intelligence. Opportunities for enhancing data analytics, reporting capabilities, and integrating advanced risk management tools are explored to elevate the system's overall functionality and contribute to more informed decision-making processes. Technical due diligence is a strategic exercise to align the financial system with industry best practices and position it for sustained growth and innovation.
Moreover, the due diligence process sheds light on the system's scalability, ensuring it can accommodate the increasing demands of transaction volumes and data processing. Opportunities to streamline operations, reduce operational costs, and enhance user experience are thoroughly evaluated. By conducting technical due diligence, organizations can proactively identify areas for improvement, enabling them to make informed decisions about technology investments, system upgrades, or potential system replacements. This strategic assessment safeguards against potential risks and positions financial systems to capitalize on emerging opportunities and stay ahead in an ever-evolving financial landscape.
Here are some examples of the areas assessed that relate to the finance industry:
1. Security and Compliance
Evaluate the security measures in place specifically tailored to financial data protection. Ensure compliance with financial regulations such as Basel III, Dodd-Frank, or specific regulatory requirements for the financial sector.
2. Financial Data Management
Scrutinize how financial data is collected, processed, stored, and transmitted within the system. Assess measures in place to ensure financial data's accuracy, integrity, and confidentiality.
3. Regulatory Compliance For Financial Systems
Verify adherence to specific financial regulations governing the industry. This includes compliance with accounting standards (e.g., IFRS or GAAP) and regulatory reporting requirements specific to financial institutions.
4. Transaction Processing And Settlement
Assess the efficiency and accuracy of transaction processing within the financial system. Evaluate settlement processes to ensure timely and secure completion of financial transactions.
5. Algorithmic Trading Systems
For systems involved in algorithmic trading, assess the reliability, speed, and accuracy of trading algorithms. Verify compliance with regulations governing algorithmic trading practices.
6. Financial Reporting And Analytics
Evaluate the capabilities of financial reporting tools and analytics within the system. Ensure the system provides accurate and timely financial insights for decision-making and regulatory reporting.
7. Fraud Detection and Prevention
Assess the effectiveness of fraud detection and prevention mechanisms within the financial system. Ensure that the system incorporates advanced analytics and monitoring for detecting fraudulent activities.
8. Interbank Communication and Connectivity
Evaluate the communication protocols and connectivity features, especially for financial systems that involve interbank transactions. Ensure secure and reliable communication channels.
9. Financial Controls And Auditing
Review internal financial controls within the system, focusing on segregating duties and auditing capabilities. Verify the system's ability to generate audit trails for financial transactions.
10. Payment Processing And Settlement Systems
If the financial system involves payment processing, review the efficiency and security of payment settlement processes. Ensure compliance with payment industry standards. For example, the Payment Card Industry Data Security Standard (PCI DSS), is a set of security standards designed to ensure that all companies that accept, process, store, or transmit credit card information maintain a secure environment.
11. Integration With Internal/External Systems
A key challenge lies in the diversity of legacy systems in which financial institutions often operate. These systems, developed over decades, vary widely in architecture and technology, making seamless integration complex. For instance, a bank aiming to integrate a modern customer relationship management (CRM) system may face difficulties aligning it with an older legacy core banking system, highlighting the challenge of bridging the technological gap between legacy and modern technologies. Interoperability challenges arise due to the diverse standards employed by different financial systems. The integration process must navigate varied data formats and communication protocols to ensure seamless system interaction.
Sensitivities Within The Financial Industry
Financial systems are highly sensitive due to the critical nature of the data they handle and the potential impact on individuals, businesses, and the overall economy. Some particular sensitivities associated with financial systems include:
Confidentiality of Financial Data: Financial systems contain highly confidential and sensitive information, including personal details, account numbers, transaction histories, and other financial data. Unauthorized access to this information can lead to identity theft, fraud, and financial loss.
Data Integrity: Ensuring the accuracy and integrity of financial data is paramount. Any manipulation or unauthorized alteration of financial records can have severe consequences, leading to incorrect financial decisions, legal issues, and reputational damage.
Operational Continuity: Financial systems must ensure continuous operation to support critical functions such as payments, transactions, and account management. Downtime or disruptions can result in financial losses, damage to reputation, and hinder the ability to meet customer expectations.
Regulatory Compliance: Financial institutions must adhere to complex regulations and compliance standards. Failure to comply with these regulations can result in legal action, fines, and reputational damage, impacting the financial institution's stability.
Cybersecurity Threats: Financial systems are prime targets for cyberattacks due to the valuable data they store. Hacking, ransomware, and phishing attacks can compromise sensitive information, disrupt operations, and erode customer trust.
Market Volatility: Financial systems are susceptible to market fluctuations and economic uncertainties. Sudden market changes can impact investments, asset values, and financial stability, affecting financial institutions and their customers.
Credit and Counterparty Risks: Financial systems often involve lending and credit activities. Assessing and managing credit risks is crucial to avoid defaults and financial losses. Counterparty risks must also be carefully evaluated, especially in complex financial transactions.
Systemic Risks: Financial systems are interconnected, and disruptions in one part of the system can have cascading effects. Systemic risks, such as a financial crisis or economic downturn, can impact the entire financial ecosystem, leading to widespread consequences.
Money Laundering and Fraud: Financial systems are vulnerable to money laundering activities and fraudulent transactions. Ensuring robust anti-money laundering (AML) measures and fraud detection mechanisms is essential to safeguard the integrity of financial transactions.
Technological Risks: Rapid technological advancements bring both opportunities and risks. Financial systems must keep pace with technological innovations while addressing risks associated with system vulnerabilities, data breaches, and outdated infrastructure.
Consumer Trust and Reputation: Maintaining the trust of customers is crucial for financial institutions. Any breaches of security, privacy, or ethical concerns can result in a loss of trust, leading to customer attrition and reputational damage.
Examples Of Significant Issues Found In the Finance Sector
The following “high-profile” examples highlight financial systems' diverse risks, including cybersecurity threats, operational vulnerabilities, and regulatory compliance challenges. Such incidents emphasize the need for continuous vigilance, robust risk management frameworks, and proactive measures to safeguard the integrity and security of financial systems.
SWIFT System Hacks (2016-2018)
The SWIFT (Society for Worldwide Interbank Financial Telecommunication) system, which facilitates secure financial messaging and international fund transfers between banks, faced multiple cyberattacks between 2016 and 2018. In these incidents, hackers targeted the vulnerabilities in the systems of several banks connected to SWIFT, manipulating the messaging system to initiate fraudulent transactions. Notable cases included the Bangladesh Bank heist in 2016, where attackers attempted to steal nearly $1 billion but were partially thwarted. These incidents raised concerns about the security of global financial messaging systems and prompted increased scrutiny and security enhancements within the SWIFT network.
Equifax Data Breach (2017)
In recent years, one of the most significant public risks in financial systems was the Equifax data breach in 2017. Equifax, one of the major credit reporting agencies, suffered a massive cyberattack that exposed the sensitive personal information of nearly 147 million consumers. The breach included names, Social Security numbers, birth dates, and, in some cases, credit card information. The attackers exploited a vulnerability in Equifax's website software, highlighting the importance of robust cybersecurity measures in financial systems. The incident had severe repercussions, leading to legal and regulatory actions, financial losses, and a significant impact on consumers' trust in the credit reporting system.
Danske Bank Money Laundering Scandal (2017-2018)
Danske Bank, one of the largest financial institutions in Denmark, faced a high-profile money laundering scandal involving its Estonian branch. Between 2007 and 2015, approximately €200 billion in suspicious transactions flowed through Danske Bank's Estonian operations, with a significant portion believed to be illicit funds. The scandal exposed weaknesses in the bank's anti-money laundering controls and raised questions about the effectiveness of regulatory oversight. The incident had severe financial and reputational consequences for Danske Bank, resulting in legal actions, fines, and increased scrutiny of anti-money laundering practices across the financial industry.
Capital One Data Breach (2019)
In 2019, Capital One, a major financial institution, experienced a significant data breach that exposed the personal information of over 100 million customers. The breach occurred when a former employee of a cloud service provider exploited a vulnerability in the bank's infrastructure, gaining unauthorized access to sensitive customer data. The compromised information included names, addresses, credit scores, and social security numbers. The incident underscored the importance of secure cloud configurations and raised concerns about the potential risks associated with third-party service providers in the financial sector.
Robinhood Trading Platform Outages (2020)
In 2020, the popular commission-free trading platform, Robinhood, experienced multiple outages during periods of high market volatility. These outages prevented users from accessing the platform, executing trades, and managing their investments during critical market moments. The incidents raised concerns about the platform's infrastructure resilience, capacity planning, and the potential impact on users' ability to react swiftly to market changes. The public nature of the disruptions led to increased regulatory scrutiny and legal challenges for Robinhood, highlighting the importance of robust and scalable technology infrastructure in financial systems to ensure uninterrupted service during market fluctuations.
Risks RingStone Has Observed During Technical Due Diligence In Financials
RingStone has undertaken comprehensive technical due diligence on various companies spanning different areas within the finance sector. Some examples include:
Software solutions across wealth advisory, superannuation, trading, and market data
Investment management solutions covering portfolio management, risk and compliance, performance measurement, and reporting
Securities finance sector: electronic trading and post-trade services
Property tax appeal services
Platforms that support banking system modernization/transformation
Here are some examples of typical risks found during technical due diligences by RingStone’s practitioners across the finance industry:
Product organization biases limited adaptability, resulting in a lack of responsiveness to market changes, customer needs, or emerging trends, hindering the ability to pivot and innovate swiftly.
A top-down tech-driven approach led to high R&D spending without balance, resulting in inefficient resource allocation, potentially leading to inflated R&D costs and limiting investment in critical areas such as customer experience and market expansion.
Weak historic portfolio planning resulted in a mixed technology stack and high costs, leading to unnecessary technology expenses, difficulties in maintaining disparate systems, and increased operational costs.
An unrationalized portfolio of products led to difficult-to-maintain and costly siloed teams and technologies, resulting in challenges in maintaining, updating, and supporting various products, increasing operational costs and potentially impacting customer satisfaction.
Challenges in attracting new talent risked future maintainability, impacting the ability to maintain and enhance the system, leading to potential technological stagnation, reduced competitiveness, and challenges in addressing evolving business needs.
Increased project complexity in data flow led to higher ongoing costs, resulting in elevated maintenance costs, difficulties integrating new features, and challenges adapting to changing business requirements.
Above-average spending on testing and maintenance due to legacy languages limited investment in innovation, new features, or strategic initiatives, potentially hindering the system's competitiveness and responsiveness to market changes.
High R&D spending, exceeding industry benchmarks, impacted profitability and financial health, potentially leading to resource constraints for critical business functions or future investments.
Weak local management impacted efficiency and productivity, leading to lower productivity, higher attrition rates, and challenges in maintaining service quality, impacting customer satisfaction and operational efficiency.
Non-optimal organizational design and Scrum setup resulted in inefficiencies, project delays, increased costs, and challenges in aligning development efforts with business goals, impacting overall project success.
Lack of product management process maturity affected roadmap planning, hindering effective roadmap planning potentially leading to delays in feature delivery, customer dissatisfaction, and missed market opportunities.
Product complexity and configuration challenges impacted adoption, resulting in longer implementation times, challenges in customer adoption, and difficulties in scaling customer services, impacting overall product adoption and revenue growth.
Limited test automation and a relatively high number of defects on Spire led to increased support costs, longer resolution times, and challenges in maintaining a high-quality product, potentially affecting customer satisfaction.
A high percentage of customer support tickets escalated to engineering affected customer satisfaction, indicating product quality issues and potentially impacting customer trust and the overall perception of the product's reliability.
Continued dependency on founder knowledge hindered scaling efforts, creating scalability challenges, potential decision-making bottlenecks, and difficulties adapting to changing market dynamics.
Limited security ownership impacting customer trust resulted in limited security practices, data breaches, eroded customer trust, damaged the company's reputation, and potential legal and regulatory consequences.
Outdated packages increasing security vulnerabilities exposed the system to cyber threats, leading to potential data breaches, service disruptions, and financial losses.
Immature security practices and the absence of penetration testing resulted in insufficient security practices, undetected vulnerabilities, increased risk of security incidents, reputational damage, and potential legal and regulatory consequences.
Suboptimal defect management affecting observability hindered identifying, prioritizing, and addressing issues, impacting overall system observability, customer satisfaction, and operational efficiency.
To address these kinds of risks, a comprehensive strategy must be implemented across the businesses. This includes streamlining platform development processes, enhancing adaptability within product organizations, establishing a balanced approach to tech-driven initiatives, improving historic portfolio planning, and rationalizing product offerings.
Additionally, efforts must be directed toward hiring and retaining skilled talent, updating legacy languages, reducing project complexity, optimizing organizational design, and enhancing product management processes. Emphasis should be placed on reducing excessive R&D spend, strengthening local management, standardizing product outputs, simplifying product adoption through configuration-driven products, and accelerating test automation.
Furthermore, enhancing security practices, addressing outdated packages, and implementing robust penetration testing is crucial for mitigating security vulnerabilities. Improving testing practices, defect management, and maintaining a strong focus on security are key components to ensure overall product quality, customer satisfaction, and operational efficiency.
How AI Can Benefit The Finance Industry
Leveraging Artificial Intelligence (AI) in the finance industry is important to driving transformative advancements and addressing the evolving needs of this dynamic sector. AI offers unparalleled capabilities to analyze vast datasets, identify patterns, and make data-driven decisions at unprecedented speeds. This translates to enhanced risk management, fraud detection, and personalized customer experiences in the financial landscape.
AI Is Not Just A Technological Choice But A Strategic Imperative That Fosters Innovation, Competitiveness, And Sustainable Growth
Integrating AI technologies, such as machine learning and predictive analytics, streamlines operational processes and enables institutions to gain deeper insights into market trends, optimize investment strategies, and improve overall efficiency. Additionally, AI-powered chatbots and virtual assistants contribute to superior customer service by providing real-time assistance and personalized recommendations. As the finance industry continues to navigate complexities, embracing AI is not just a technological choice but a strategic imperative that fosters innovation, competitiveness, and sustainable growth.
1. Fraud Detection and Prevention
Business Benefits: AI can significantly enhance fraud detection and prevention in the finance industry by analyzing vast datasets to identify patterns indicative of fraudulent activities. This proactive approach helps financial institutions mitigate risks and minimize financial losses.
User Benefits: Users benefit from increased security and reduced likelihood of unauthorized access to their accounts. Real-time fraud detection ensures timely intervention, protecting users from financial harm and maintaining trust in the financial system.
2. Personalized Financial Advice
Business Benefits: AI-powered robo-advisors can provide personalized financial advice based on individual user profiles, financial goals, and market conditions. This automation allows financial institutions to offer scalable advisory services to a broader audience.
User Benefits: Users receive tailored financial recommendations, investment strategies, and budgeting advice, leading to better-informed financial decisions. Personalization enhances the user experience and fosters a sense of financial empowerment.
3. Customer Service Automation
Business Benefits: AI-driven chatbots and virtual assistants can automate routine customer service tasks, such as account inquiries and transaction verifications, reducing operational costs and improving efficiency.
User Benefits: Users experience faster response times, 24/7 availability, and seamless interactions with AI-powered assistants. Quick issue resolution and accessibility contribute to an enhanced customer experience.
4. Credit Scoring and Risk Assessment
Business Benefits: AI algorithms can analyze diverse data sources to evaluate creditworthiness and assess risk more accurately. This leads to improved lending decisions and risk management strategies for financial institutions.
User Benefits: Users with limited credit histories or unconventional financial backgrounds may benefit from more inclusive and fair credit assessments. AI-driven credit scoring can expand access to financial services for a broader range of individuals.
5. Automated Regulatory Compliance
Business Benefits: AI can assist financial institutions in automating compliance processes by continuously monitoring regulatory changes, updating policies, and ensuring adherence to evolving legal requirements.
User Benefits: Users benefit indirectly through enhanced security and data protection. Automated compliance measures provide a more secure and transparent financial environment, safeguarding user interests.
6. Predictive Analytics for Investment
Business Benefits: AI-powered predictive analytics can analyze market trends, economic indicators, and company performance to make informed investment decisions. Financial institutions can optimize portfolio management and asset allocation.
User Benefits: Users can access more informed investment strategies, potentially leading to improved investment returns. AI-driven insights empower users to make data-driven investment choices aligned with their financial goals.
7. Intelligent Virtual Assistants for Banking
Business Benefits: AI-driven virtual assistants can handle various banking tasks, from account inquiries to fund transfers. This automation streamlines processes, reduces operational costs and improves overall efficiency.
User Benefits: Users experience convenient and efficient banking interactions. Intelligent virtual assistants provide quick and accurate responses to queries, enhancing the overall user experience.
Conclusion
Organizations Can Mitigate The Likelihood Of Data Breaches, Operational Disruptions, And Legal Repercussions By Scrutinizing Aspects Such As Cybersecurity Measures, Data Integrity, Scalability, And Compliance With Industry Regulations
Conducting thorough technical due diligence on financial systems is imperative in ensuring the resilience, security, integration and efficiency of the underlying technology supporting critical financial operations. This proactive examination allows organizations to identify and address potential risks that, if left unattended, could have severe consequences, ranging from financial losses to reputational damage, as illustrated in this blog. The complexities of financial systems, coupled with the evolving nature of technology and regulatory landscapes, make diligent assessments crucial for safeguarding the integrity of these systems.
Technical due diligence serves as a preemptive measure to avoid key risks associated with financial systems. Organizations can mitigate the likelihood of data breaches, operational disruptions, and legal repercussions by scrutinizing cybersecurity measures, data integrity, scalability, and compliance with industry regulations. Identifying these risks in advance allows for strategic planning and implementation of robust security measures, ensuring the ongoing stability of financial operations and bolstering user trust in the financial ecosystem.
Integrating Artificial Intelligence (AI) presents a transformative force within the financial industry, offering key business benefits. AI enhances fraud detection, enabling financial institutions to identify and prevent unauthorized activities proactively. Personalized financial advice powered by AI-driven robo-advisors benefits users with tailored recommendations and allows financial institutions to offer scalable advisory services, expanding their reach.
Automating customer service tasks through AI-driven chatbots improves operational efficiency, providing users with quicker issue resolution and seamless interactions. AI's role in credit scoring and risk assessment promotes fairer evaluations, potentially broadening access to financial services. Automated regulatory compliance ensures adherence to evolving legal requirements, reducing the risk of non-compliance issues.
In conclusion, technical due diligence on financial systems is a strategic imperative to identify, mitigate, and avoid key risks. The integration of AI introduces a paradigm shift, offering unparalleled business benefits such as enhanced security, personalized services, and improved operational efficiency. By embracing AI and conducting thorough technical due diligence, the financial industry positions itself for sustained growth, innovation, and the delivery of resilient, customer-centric financial services.
About The Authors
Clinton Browne has over 28 years of IT experience, progressing from an engineer to holding C-Suite positions such as CTO/CIO. His leadership extends across various domains, including Cloud, Software Development, Agile/DevOps, and Best Practice/Process Improvements. Clinton is dedicated to technology and innovation, delivering high-quality products and services while fostering successful teams and solutions.
His expertise in the finance industry is highlighted by his role as CTO for a well-established FinTech organization and as the architect behind the first omnichannel Banking-as-a-Service platform. Before his current position at RingStone, Clinton served as the CIO of a mobile-first challenger bank, showcasing a comprehensive background in financial technology. Contact Clinton at clinton.browne@ringstonetech.com.
Jon White is an experienced technology leader with over 34 years of international experience in the software industry, having worked in the UK, Malaysia, Bulgaria, and Estonia. He holds a BSc (Hons) in Systems Design.
Jon has held multiple leadership positions throughout his career across various sectors, including banking/financial services, loyalty management, internet telecoms (Skype), IT service management, and real estate.
Jon is recognized for his expertise in Agile software development, particularly helping organizations transform to Agile ways of working (esp. Scrum), and is a specialist in technical due diligence. Over the last few years, he has completed over a hundred due diligence and assessment projects for clients, including private equity, portfolio companies, and technology companies, spanning multiple sectors. Contact Jon at jon.white@ringstonetech.com.