The hidden power of small language models in banking
FEB. 24, 2025
2 Min Read
Small but mighty: How specialized AI is reshaping finance now.
While large language models like GPT-4, Gemini, and Claude continue to capture headlines, specialized small language models (SLMs) are quietly revolutionizing how organizations operate. In the banking sector, SLMs are used to run targeted applications in contract analysis, customer onboarding, customer service, fraud prevention, risk assessment, and more. The value of these smaller models trained on specialized data is that they’re faster to implement, easier to control, and they provide a measurable ROI while working seamlessly with proprietary data and existing workflows.
As financial institutions look to become more agile while still operating in a highly regulated environment, SLMs are set to become core features in their tech stacks.
Small models are winning the enterprise AI race
The ongoing race to adopt AI capabilities has led many enterprises to experiment with large language models. However, organizations across industries are discovering that having smaller, specialized models can enhance their ability to execute specific tasks.
Recent research from S&P Global Market Intelligence shows that organizations across all AI maturity levels are using 158 different AI models — and that number is expected to reach 176 within the next year. This proliferation of focused models highlights that targeted, specialized AI solutions often deliver better results than a one-size-fits-all approach.
The advantages are clear. Unlike massive models that require extensive computational resources to provide generalized capabilities, SLMs are able to deliver focused functionality while demanding fewer resources. This shift toward smaller models provides both efficiency and effectiveness. SLMs enable faster deployment, provide greater transparency in their decision-making processes, and can be fine-tuned for specific business needs.
This is particularly important for financial institutions. Operating in a highly regulated industry requires AI solutions that are transparent and controllable, as well as powerful. SLMs make it easier to achieve this balance. Their combination of focused capability and transparency makes them increasingly attractive to banking leaders looking to drive innovation while managing risk.
High-impact applications reshaping banking operations
Across banking operations, SLMs trained on specific institutional and industry data can deliver measurable improvements in efficiency, accuracy, and risk management. Leading institutions have started deploying these specialized models to address specific operational challenges, including fraud detection, customer onboarding, and more.
Contract intelligence has reached a new frontier
The analysis of legal and compliance documents has traditionally been a resource-intensive process prone to human error. Specialized SLMs are transforming this landscape by automating the extraction and analysis of key information from contracts, regulatory filings, and compliance documents. These models can be trained on specific legal frameworks and banking regulations, achieving vastly improved accuracy rates in document classification and information extraction tasks.
Customer service excellence at enterprise scale
When it comes to customer service in banking, SLMs aren’t replacing the human touch, they’re enhancing it. Specially trained models powered by conversational analytics, customer behavior, and any internal customer data excel at analyzing customer interactions and providing relevant information to service representatives in real time.
The new science of fraud prevention
In fraud prevention, speed and accuracy are paramount. With global fraud losses totaling $485.6 billion in 2023 alone, banks need more sophisticated approaches to combat increasingly complex threats.
SLMs trained on specific fraud patterns can analyze transactions and identify suspicious activity almost immediately. This efficiency and high accuracy rate means that teams can significantly reduce false positives while also increasing fraud detection rates.
Fraud analytics SLMs can specifically be used to power:
- Account takeover prevention: SLMs can analyze login patterns and changes in user behavior in real time to flag potential account takeover attacks before significant damage occurs.
- Money laundering activities: Specially trained SLMs can automate tasks that commonly require significant manual intervention (and are therefore prone to human error). These include transaction monitoring, alert investigation, and smart regulatory reporting.
- Payment fraud detection: In the rapidly growing realm of digital payments, SLMs excel at rapid pattern recognition. They can process vast amounts of transaction data in real time to identify potential fraud.
The key advantage of using SLMs for fraud prevention lies in their ability to be continuously updated and fine-tuned based on new threat patterns. This ensures banks can stay ahead of emerging fraud schemes while maintaining operational efficiency.
Credit risk assessment for the digital age
Smart algorithms trained on specific lending criteria and historical data are changing the game for credit risk assessment. SLMs can be trained with a bank’s risk exposure policies and past lending information to make preliminary decisions on new applications. These models can process applications faster while maintaining rigorous standards and ensuring that financial institutions don’t put themselves at risk by incurring high default rates.
Compliance is moving from reactive to predictive
Real-time compliance monitoring is becoming a reality through SLMs trained on specific regulatory frameworks. These models can continuously analyze transactions and activities against compliance requirements, flagging potential issues before they become violations. In addition, these SLMs can stay on top of any regulatory changes and continually ensure that the financial institution remains compliant with new rules.
The business case for small language models
The advantages of SLMs extend far beyond technical capabilities, delivering strategic value across three critical dimensions of banking operations. With research showing that 80% of organizations expect to adopt AI across all business functions in the next two years, banks need practical, focused solutions that can be implemented efficiently across their operations while maintaining control and compliance. Small language models do just that.Operational efficiency
SLMs have the power to fundamentally transform how banks handle day-to-day operations. Rather than forcing institutions to rebuild their processes around a general-purpose AI solution, these specialized models can integrate more seamlessly into existing workflows. They augment human capabilities in specific tasks—from document processing to customer service—and allow staff to focus on higher-value activities that require judgment and personal interaction.
Business growth
In the financial industry, SLMs have the potential to drive business growth, especially in areas where specialized expertise leads to competitive advantage. For starters, specially trained small language models can accelerate the decision-making process and enable more personalized customer experiences, therefore expanding customer loyalty and advocacy. In addition, SLMs could also be implemented to scale operations more efficiently. Rather than adding staff to handle increased transaction volumes or enter new markets, banks can leverage SLMs to automate routine decisions while ensuring their specialists focus on complex cases that require human judgment.
Risk management
Small language models excel at helping banks operate with precision because they can be trained on specific risk scenarios and institutional policies. This makes them more reliable for critical decision-making than larger, general-purpose language models. Perhaps most importantly, these models can be rapidly updated as new risks emerge, ensuring banks stay ahead of evolving threats without compromising accuracy.
Regulatory compliance
Supporting regulatory compliance is where SLMs demonstrate one of their greatest advantages over large language models.
Banks face a significant challenge in adopting large language models (LLMs) due to the opacity of data sources behind model outputs. This poses a particular challenge for regulatory compliance, especially when these models influence decisions about customer treatment and pricing. Smaller language models may offer a more viable solution to this challenge, as they provide a clearer path to understanding and documenting the underlying data sources.
The transparency of SLMs is invaluable here. By nature, smaller, specialized models provide clear decision trails that can be audited and validated. Plus, banks can train these models on specific regulatory frameworks and institutional policies, therefore ensuring consistent compliance while maintaining efficiency.
Accelerating your AI journey
To successfully implement SLMs, banks should follow a structured approach:
- Start with clearly defined use cases that align with your business objectives.
- Build or acquire models specifically trained for these use cases.
- Implement robust testing and validation processes.
- Scale gradually based on demonstrated success.
Strategic partnerships will be crucial for accelerating this journey. Look for partners with deep domain expertise in both banking and AI implementation who can help navigate the technical and regulatory challenges while ensuring rapid time to value.
The future of banking is focused
For banks looking to stay competitive in an increasingly digital landscape, the path forward is clear. Choose targeted, efficient AI solutions that deliver measurable business value. Small language models represent both a smart technological choice and a strategic imperative for institutions committed to meaningful transformation.
As the financial services industry continues to evolve, the winners won’t be those with the biggest AI models, but those who deploy the right AI for the right purpose. This will require agility and smart decision-making, keeping compliance, business value, and the balance of human impact top of mind. Banks that recognize and act on this reality today will be better positioned to lead as the industry continues to leverage AI and its evolutions.
