The financial services industry is at the forefront of the data transformation era, leveraging data, analytics, and machine learning to optimize a wide range of functions. From credit card processing and insurance underwriting to retail banking, data is reshaping the way these organizations operate. By implementing AI applications effectively, financial services companies can navigate strict regulations while achieving meaningful, value-driven outcomes.
In this post, we’ll explore some of the most impactful AI applications in financial services, outlining their benefits and sharing real-world examples from phData that highlight their transformative potential.
What are the Top Applications of AI for Financial Services?
At phData, we have had the privilege of helping many leading financial services companies better harness AI to make more informed decisions. Based on our experience, financial services customers have the most success (in terms of adopting AI) with the following applications:
Modern Invoice Processing
Financial institutions may receive upwards of 35,000 invoices monthly, many of which require special attention to process. With invoices received in varying formats and from different sources, existing procedures for invoice processing may take up to 20 minutes per invoice.
Existing invoice processing pipelines often require dozens of contractors to manually collect all relevant data from source systems, such as Sage Intacct, and load the data into destinations, such as Bill.com or Vision HelpDesk. Before the payment can be approved and the invoice is considered processed, these contractors must then manually match customer-to-account information.
Finally, once the invoice is processed, contractors must manually gather reports from multiple spreadsheets to share with leadership. As invoice quantities continue to increase, organizations need a solution that can automate and modernize this process. This is where AI truly shines.
phData’s Approach
One of our largest financial services customers was struggling to keep up with the growing demand for invoice processing. To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud, Fivetran, and Azure to automate invoice processing. Ultimately, the AI solution improved invoice processing speeds from 20 minutes per invoice to 1 minute per invoice for the client’s Accounts Receivable function.
The AI invoice processing pipeline automates data ingestion and matches customer-to-account information. Additionally, phData enhanced the payment approval UI and presented a clean and consolidated view of the data, allowing analysts to generate reports without manual curation.
Impact
- 1,500 hours saved per month
- 20x invoice processing speed improvement
- Streamlined process that saves time and resources
- Increased bandwidth to handle more invoice volume
- Enhanced accuracy and data quality
- Reduced manual efforts
- Scalable processes
External AI Chatbots
Insurance and retirement agencies aim to help their customers build secure futures through insurance, investments, and retirement solutions. It is common for organizations to receive 250+ questions per month related to customer contracts. Correspondingly, the business must dedicate full-time resources to answering these customer inquiries.
An industry average response time is around three days, often requiring multiple clarification email exchanges. Many institutions (including a few of our clients) are seeing a 40 percent increase in questions year over year. Without an AI application in place, organizations experience barriers to scaling, poor customer experience, lengthy response times, and missed business goals.
phData’s Approach
To solve this challenge, phData created a custom chatbot that retrieves contract language quickly and accurately. Once the customer submits a contract question, an associate can submit the question to a Retrieval Augmented Generation (RAG) chatbot. The AI chatbot then automates the retrieval of relevant contract language, allowing for quick querying of large amounts of text (including call transcripts, contracts, knowledge bases, and wikis) for relevant information.
Impact
- 40% expected yearly increase in annual customer questions will not require an increase in labor investment
- $400k+ yearly savings in reducing manual work of FTEs
- <5 seconds automatic retrieval of contract information
- 70% reduction in client query response times
This solution provides organizations with a cost-effective, straightforward way to start leveraging AI. As an institution’s first AI deployment, an AI chatbot demonstrates the incremental value of AI, setting the stage for future projects to build upon its success.
AI Compliance Monitoring
Compliance monitoring consists of processes that ensure organizations follow all relevant laws, regulations, and internal policies. Compliance monitoring often requires manual processes, which leads businesses to hire additional full-time employees as their companies grow. Implementing AI compliance monitoring can help organizations significantly reduce manual labor and, correspondingly, full-time employee hiring.
By managing and curating highly relevant metadata, it becomes easier to develop data products that enable regulatory compliance and reporting. Non-compliant invoices, particularly those involving bribery, posed a significant risk due to potential reputational damage.
Let’s take a look at one of our clients that had a manual review process of around 25 non-compliant invoices per quarter, with a constant chance of oversight.
phData’s Approach
phData implemented Optical Character Recognition (OCR), which was performed using the open-source tool Paddle (Parallel Distributed Deep Learning). Raw text was extracted and passed to a language model (Mixtral 8x7Bl via Snowflake Cortex) to be structured. The text was then passed to a translation service (DeepL), and the output was passed to a final language model (Mistral Large) for evaluation.
Aligned with recent DOJ guidance, phData developed an automated process leveraging Dataiku and Snowflake’s Cortex to translate invoices and detect potential bribery. This empowered the customer to ensure compliance on all invoices and significantly enhanced risk management beyond the previous quarterly limit.
Machine Learning Operations (MLOps)
Today, businesses are eager to implement AI systems in their workplace in order to stay at the forefront of the industry and keep up with competitors. However, just because the wheels are turning, does not mean that they are moving in the right direction. The data scientists at one mortgage insurance company had developed dozens of ML models using a wide array of tools and technologies, and they had no means for operationalizing those models.
phData’s Approach
phData designed and architected an MLOps platform on AWS and Snowflake aimed around the following capabilities:
- Reliability
- Repeatability
- Observability
- Governance
Impact
- MVP MLOps platform implemented in 12 weeks
- The customer was able to deploy 10+ models in just the first year
Conclusion
The financial services sector sits right at the crux of AI innovation. With the emergence of Large Language Models (LLMs) and other advanced AI tooling, there is an influx of exciting applications for improved organizational functionality. Although supervised and unsupervised learning algorithms still have their place within fraud detection and risk evaluation models, today we stand at an exciting juncture where real business value can be extracted from Generative AI and novel AI technologies.
If any of the AI applications covered in this blog interest you, phData can help your business implement them. phData helps with the entire data and analytics lifecycle – from strategy to implementation – so you can make sense of your data and use it to solve complex business problems.
FAQs
What are the most common projects in financial services?
Migrations from legacy on-prem systems to cloud data platforms like Snowflake and Redshift.
Creation of dashboards and reports for descriptive analytics.
Development of AI/ML predictive applications for risk evaluation, early warning, and automated decisioning.
Implementation of metadata-driven data pipelines for governance and reporting.
When it comes to data modernization, what are the largest challenges facing financial institutions?
Financial institutions face a high degree of risk, regulatory requirements, and governance. Any data-driven system introduces complexity that must be adapted to meet those requirements. While automation is key to solving those problems, developing appropriate checks and balances requires careful attention to stakeholders and business needs.
What are the largest areas of opportunity for data-driven modernization?
The modern economy runs on data, and financial institutions have always relied on data within their business. New technologies have opened the doors for groundbreaking transformations, including:
Automation of claim and application reviews
Fraud detection and active alerting
Real-time reporting on portfolio performance
Early warning systems for account delinquency
