In today’s fast-paced financial landscape, institutions must strike a delicate balance between delivering a seamless customer experience, managing risk in real time, and preventing fraud before it occurs.
However, these critical functions often operate in isolation, leading to inefficiencies, increased operational costs, and a fragmented user experience.
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The traditional siloed approach hinders the ability to detect threats quickly and compromises customer trust. To address these challenges, financial institutions must adopt a unified decision intelligence framework that integrates real-time customer analytics, risk and fraud strategies that are deployed as autonomous agents.
AI-driven, unified decisioning and autonomous agents
Despite the growing adoption of artificial intelligence (AI) in the financial sector, many banks still deploy AI solutions in isolated areas like customer service chatbots, credit risk assessments, or fraud and financial crime monitoring.
While these applications offer some benefits, they fall short of unlocking the full potential of Agentic AI. A more holistic approach, however, enables financial institutions to achieve real-time fraud detection, instant risk assessment, and seamless customer interactions within a fully governed and observable platform.
By leveraging AI and machine learning, banks can analyse vast amounts of data across multiple touchpoints, identifying patterns and anomalies that signal fraud or heightened risk. This is supplemented through real-time integration with third-party risk information. This real-time capability allows organisations to maximise the use of intelligence to prevent fraudulent transactions, while simultaneously ensuring that legitimate customers enjoy a frictionless journey.
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By GlobalDataA unified decisioning platform eliminates redundancies, enhances security, and ensures regulatory compliance, all while improving customer satisfaction.
One of the most significant advantages of this approach is its ability to connect currently siloed processes into a seamless workflow.
For example, a new customer applying for a loan would traditionally undergo multiple disconnected steps, such as Know Your Customer (KYC) verification, fraud checks, risk assessments, and the final loan decision. Each of these steps operates independently, often causing delays or friction.
With a unified AI-driven approach, these processes can be integrated into a single workflow, where real-time AI models assess risk, verify identity, and detect fraud in parallel, allowing for instant decision-making. This means that instead of waiting days for approval, a customer can receive a loan decision within seconds.
The result is an improved experience, enhanced regulatory compliance, and reduced financial risk for the institution. This transformation is made possible through intelligent automation and a cohesive decisioning strategy.
The role of real-time analytics and automation with agents
A key advantage of unified decisioning is the ability to harness information collected in the moment, with historical customer data and further supplement that view with data points from third-party services such as bureau data or live risk monitoring.
Traditional risk and fraud detection methods rely on manual processes and rule-based systems that often lag behind evolving threats. In contrast, AI-powered decisioning systems can process and interpret large datasets in milliseconds, enabling immediate action when suspicious activity is detected.
Automation further enhances this approach by reducing human intervention in routine tasks. Commonly decision strategies are parameterised allowing analysts to apply low impact changes, which when combined with automated testing allows strategy changes to be operationalised in hours compared to the weeks financial organisations historically required.
One example is Finland’s top retail bank, S-Bank, which has leveraged advanced AI and machine learning technologies from SAS to streamline its credit scoring and enhance customer service. By automating credit risk processes, the bank has reduced loan processing times while maintaining accuracy.
With real-time decision-making and AI-driven analytics, S-Bank has strengthened customer relationships and optimised business outcomes, demonstrating how AI-powered decisioning not only enhances operational efficiency but also fosters customer trust in a competitive market. This unified approach, bringing data, analytics, and decisioning together, has enabled S-Bank to act faster, reduce silos, and drive smarter, more connected outcomes across the business.
Enhancing customer experience through AI integration
Customer expectations have evolved over recent years, with modern consumers demanding personalised, instant, and secure financial services.
To meet these expectations, it’s crucial to address the challenges posed by fragmented fraud detection and risk assessment systems. A disjointed approach can lead to delays, unnecessary security checks, and false positives, all of which can frustrate customers.
By implementing a unified AI-driven decisioning framework, financial services can seamlessly integrate fraud prevention measures, ensuring a smooth and secure experience that allows legitimate transactions to proceed without disruption.
For example, AI-powered behavioural biometrics can assess a user’s interactions in real time, such as typing speed and device usage, to verify authenticity without requiring additional verification steps. This proactive approach reduces friction, enhances security, and builds customer trust by ensuring that protective measures do not come at the expense of convenience.
The rise of agentic AI in financial services
Agentic AI is rapidly emerging as a transformative force across industries, from personal productivity to enterprise execution. Agents are recognised for their ability to orchestrate deterministic and probabilistic AI, along with the logical based rules required to put decisions into effect. Crucially they are deployed to execute independently of the environment which designed them, and by using cloud native technology (containers) they can be tested and deployed automatically. Through cloud orchestration, agents can run at scale and between availability zones or regions to provide resilience and respect data sovereignty.
In financial services, this advanced AI paradigm is playing a pivotal role in unifying decision-making processes, enabling institutions to dynamically assess risk, detect fraud, and enhance customer engagement with minimal human intervention.
By leveraging Agentic AI memory, banks can create intelligent workflows that adapt to shifting data patterns, allowing business analysts to refine business rules, or automatically update AI models.
This level of automation and intelligence is particularly impactful in areas such as credit underwriting, where AI-powered agents can continuously refine risk models based on new transaction data and fraud signals, ensuring that financial institutions stay ahead of evolving threats while delivering seamless customer experiences.
Scaling AI-driven decisioning across the industry
The success of unified decisioning in financial services highlights its potential for widespread adoption across the industry. As AI technology continues to evolve, financial institutions must embrace a strategic approach that prioritises integration and scalability.
Cloud-based AI platforms offer a cost-effective way to implement unified decisioning across multiple business functions, allowing banks to scale their operations while maintaining agility. With the cloud, banks can seamlessly integrate advanced AI capabilities, ensuring real-time data processing and decision-making across various departments, ultimately driving both efficiency and innovation.
Regulatory compliance is another crucial factor in the adoption of AI-driven decisioning. Financial institutions must ensure that their AI models align with evolving regulatory frameworks and trustworthy AI principles.
Transparent AI decision-making processes, explainable models, and robust audit trails are essential components of a responsible AI strategy that balances security, compliance, and customer experience. What is also important, within that transparent framework, is democratising access to the technology so a wide range of employees can build and deploy models – this can be achieved through low-code and no-code software that caters for employees with different levels of expertise.
The future of financial services
As the financial landscape grows increasingly complex, the need for a seamless, integrated approach to customer experience, risk management, and fraud detection has never been more critical.
AI-driven decisioning presents a powerful solution, enabling banks to break down silos, optimise risk management, and deliver an enhanced customer experience at scale.
Agents have the capacity to heighten customer interaction over the coming years – whether that’s through agents that assist employees (e.g. in call centres) being able to automatically access the relevant information rather than search for it while the customer is left waiting, or agents that can self-serve customers directly.
The path forward for financial institutions is clear: those that invest in AI-driven unified decisioning will not only enhance security and compliance but also redefine the banking experience for the digital age.
With real-time analytics, automation, and intelligent risk management, the financial sector can achieve a future where fraud prevention and customer satisfaction go hand-in-hand.
David Shannon is Head of Decisioning at SAS UK & Ireland
