読み込み中
5 Best Practices for AI in Financial Services
Aligning AI theory with business practicality is crucial for financial services. These five tasks nurture effective AI architectures.
In the financial services sector, integrating Artificial Intelligence (AI) has become pivotal. Yet ensuring the “theory” of AI matches business “practicality” remains a common challenge. Whether your AI architecture interest is front-, middle- or back-office focused, or targeting common AI financial services applications (e.g., risk, compliance, capital markets, marketing intelligence, etc,), here are five tasks to develop effective architectures. Together, they fulfill the need to balance innovation with regulatory compliance. By focusing on clear value objectives, agile AI software life cycles, trusted data foundations, and rigorous validation, financial institutions can harness the full potential of AI to enhance decision-making, improve efficiency, and gain a competitive edge.
1. Define value objectives
Embarking on AI initiatives without a well-defined business problem can lead to misaligned solutions. Specific “value-based” objectives must be set before diving into tech implementation. For instance, if a financial institution aims to enhance fraud detection, understanding the unique patterns of fraudulent behavior pertinent to their operations is essential. KYC, AML, and Transaction Monitoring have different needs and requirements in capability, response time, and action. Building to immediate and near-immediate requirements will ensure focus and demonstrable value of the AI implementations to address genuine needs, rather than building generic valueless technology swamps.
2. Design for modularity
You can build or buy based on the need, price sensitivity, and technical capability of your organization: there are nicely packaged AI solutions, there is code, and there are platform frameworks in between. Tier 2 and 3 organizations may prefer easily managed common platforms and tend toward low code, while tier 1 organizations may prefer to build custom (and legacy) solutions ground-up, often centered on the latest, greatest, or oldest legacy code. Whether building, creating, or buying AI solutions, have modules focus on pipeline tasks—data management and governance; predictive, generative, or discriminative models; scoring framework; alert; dashboard report—to allow for greater customization and adaptability, now and in the future. For example, a bank might develop an AI module that assesses credit risk by analyzing unconventional unstructured data sources, thereby gaining a competitive edge. A data layer or data product module distinct from the model and scoring layers makes it easier to apply new datasets to regulatory-proven model sets.
3. Engage agile AI software life cycles
The journey from concept to deployment needs prototyping, training, testing, deployment, and optimization phases. Initially, develop a prototype centered on a real-world use case to test the feasibility of the AI solution. Refine this prototype to ensure it meets both functional and non-functional expectations before moving to full-scale implementation. However, development shouldn't stop there. As the world changes, data and knowledge learnt from the production “edge” needs to reinforce the ongoing tuning and feature selection in the core model. By minimizing the gap between design and production, you can remain agile and highly responsive, and mitigate risks in deploying robust and adaptable AI systems.
Yes, this is surely a good software development life cycle, but the exceptional sensitivity of AI models to the quality of the data that drives them requires care and attention.
4. Know your trusted data foundation
AI solutions are both resource-intensive and data-intensive. To ensure model accuracy and process efficiency (for example, by reducing waste from false positives), it's crucial to evaluate and understand how data models interact. Where that data-model intersection captures, and impacts, the ever-changing aspects of people, places, businesses, lives, and organizations—i.e., entities—your data foundation must be crystal clear and transform with the world. Avoid isolating data in siloed databases. Instead, ensure data is readily available at the point of need, leveraging your organization's complete data estate. You can address and resolve any duplicated data, and make sure your data does not imply connections when none exist, such as in cases of “overlinking.” It's essential to recognize genuine connections and events. If you miss that watchlist mention, or overlook a dubious partnership with a known fraudster, or discover only too late that your customer is engaged elsewhere, your operational decisions become more than just meaningless garbage-in, garbage-out (GIGO) data analytics textbook exercises. They become costly operational risks.
5. Rigorously validate and explain
Ensuring the reliability of AI outputs is paramount in regulated financial services. This is in part driven by long-standing governance frameworks such as SR11/7 and BCBS 239, where model, data, and process validation and transparency is an absolute requirement. It is also embedded in operational regimes such as the EU’s DORA Act targeting operational resilience, where model behavior needs to be explainable. More recently, dedicated AI regulations for example, the EU AI Act), get specific into how AI is implemented, highlighting issues such as bias in addition to traditional transparency risks. All require a comprehensive validation framework to assess the usability of results. Incorporating a human-in-the-loop approach is essential when automation falls short of expectations—and even when it doesn’t. For example, in investment management, AI-generated insights should be reviewed by financial analysts to confirm their validity before making strategic decisions.
By adhering to these principles, financial services professionals can effectively integrate AI into their operations, enhancing decision-making, improving efficiency, and creating strategic advantage in the competitive financial landscape.
The Quantexa Decision Intelligence Platform empowers AI workflows
The Quantexa Decision Intelligence Platform delivers on all these needs, delivering trusted, meaningful data across data, AI, and model pipelines:
Value: Quantexa has been developed not for technology’s sake, but to service pipelines of key use cases: AML, KYC, Marketing, Insurance, Credit Risk, Supply Chain, ESG, etc.
Modularity: The trusted Quantexa data foundation, driven by its contextual fabric, is flexed to the particular requirements of the data and model pipelines, from ingestion to analytic to output and decision.
Agile: Whether the handover is between prototype and operation, between data and model, or among data engineers, data scientists, data analysts and SME, the Quantexa Contextual Fabric ensures your data that powers your AI pipeline is as agile as your life cycle.
Know: Quantexa resolves and assesses entities where and how needed, ensuring your data estate, not just your database, is both accurate and ready for action.
Validate: Quantexa facilitates AI though provision of explainable context, explainable through graph processes
Explore the Quantexa Platform to find out more.
