With the advent of generative AI solutions, a paradigm shift is underway across industries, driven by organizations embracing foundation models (FMs) to unlock unprecedented opportunities. Amazon Bedrock has emerged as the preferred choice for numerous customers seeking to innovate and launch generative AI applications, leading to an exponential surge in demand for model inference capabilities. Amazon Bedrock customers aim to scale their worldwide applications to accommodate a variety of use cases. One such customer is FloQast.
Since its founding in 2013, FloQast has had the privilege of working with over 2,800 organizations across various industries and regions, helping them streamline their accounting operations. From automated reconciliations to tools that manage the entire close process, FloQast has seen firsthand how organizations, big and small, struggle to keep pace with their accounting needs as they scale. FloQast’s software (created by accountants, for accountants) brings AI and automation innovation into everyday accounting workflows. You can reconcile bank statements against internal ledgers, get real-time visibility into financial operations, and much more.
In this post, we share how FloQast built an AI-powered accounting transaction solution using Anthropic’s Claude 3 on Amazon Bedrock.
Accounting operations: Complexity amplified at scale
At the heart of every successful organization—whether small startups or large corporations—lies a well-oiled financial and accounting operation. Accounting is more than just a back-office function; it’s the backbone of every business. From processing payroll to generating financial statements, accounting is a ubiquitous force that touches every facet of business operations.
Consider this: when you sign in to a software system, a log is recorded to make sure there’s an accurate record of activity—essential for accountability and security. Similarly, when an incident occurs in IT, the responding team must provide a precise, documented history for future reference and troubleshooting. The same principle applies to accounting: when a financial event takes place, whether it’s receiving a bill from a vendor or signing a contract with a customer, it must be logged. These logs, known in accounting as journal entries, provide a clear financial record.
Now imagine this process scaled across hundreds, or even thousands, of transactions happening simultaneously in a large organization. The complexity of accounting increases exponentially with growth and diversification. As businesses expand, they encounter a vast array of transactions that require meticulous documentation, categorization, and reconciliation. At scale, upholding the accuracy of each financial event and maintaining compliance becomes a monumental challenge. With advancement in AI technology, the time is right to address such complexities with large language models (LLMs).
Amazon Bedrock has helped democratize access to LLMs, which have been challenging to host and manage. Amazon Bedrock offers a choice of industry-leading FMs along with a broad set of capabilities to build generative AI applications, simplifying development with security, privacy, and responsible AI. Because Amazon Bedrock is serverless, you don’t have to manage infrastructure to securely integrate and deploy generative AI capabilities into your application, handle spiky traffic patterns, and enable new features like cross-Region inference, which helps provide scalability and reliability across AWS Regions.
In this post, we highlight how the AI-powered accounting transformation platform uses Amazon Bedrock. FloQast addresses the most complex and custom aspects of financial processes (the final 20%)—those intricate, bespoke aspects of accounting that are highly specific to each organization and often require manual intervention. FloQast’s AI-powered solution uses advanced machine learning (ML) and natural language commands, enabling accounting teams to automate reconciliation with high accuracy and minimal technical setup.
FloQast AI Transaction Matching
Seamlessly integrated with the existing FloQast suite, the AI Transaction Matching product streamlines and automates your matching and reconciliation processes, delivering unparalleled precision and efficiency.
It offers the following key features:
- AI-driven matching – You can automatically match transactions across multiple data sources with high accuracy
- Flexible rule creation – You can use natural language to create custom matching rules tailored to your unique processes
- Exception handling – You can quickly identify and manage unmatched transactions or discrepancies
- Audit trail – You can maintain a comprehensive audit trail of matching activities for compliance and transparency
- High-volume processing – You can efficiently handle large volumes of transactions, suitable for businesses of all sizes
- Multi-source integration – You can seamlessly integrate and match transactions from various financial systems and data sources
Let’s review how it works:
- Transaction data is gathered from bank statements and enterprise resource planning (ERP) systems.
- An accountant will select specific transactions in both systems and choose Generate AI Rule.
The following screenshot shows the general ledger system on the left and the bank statement on the right.
- Based on the selected transactions, text is generated (see the following screenshot).
- At this point, the accountant has the option to either accept the generated text or edit the text.
- The accountant chooses Save and apply to generate a rule in coded format that is further used to find additional matches, helping the accountant automate transaction reconciliation.
FloQast AI Transaction Matching offers the following benefits:
- Unified environment – It seamlessly integrates with your existing FloQast products for a single source of truth
- AI-powered automation – It uses advanced ML to handle complex matching scenarios
- User-friendly interface – It’s designed by accountants for how accountants work, providing ease of use and adoption
- Real-time insights – You can gain immediate visibility into your transaction data across systems
- Scalability – It can adapt as your transaction volumes grow and business evolves
FloQast AI Annotations
FloQast’s new AI Annotations feature empowers teams to seamlessly and automatically annotate and review sample documents, streamlining compliance and audit processes through advanced automation and ML.
It offers the following key features:
- Automated document annotation – You can upload sample documents to automatically annotate key data points with attributes specified in your testing criteria, saving time on manual reviews
- AI-powered analysis – You can use advanced AI and natural language models to analyze document text, highlighting relevant information according to predefined controls and testing attributes
- Bulk annotation for efficiency – You can select multiple documents or testing controls for bulk annotation, reducing time spent on repetitive document processing
- Structured storage and audit trail – You can maintain a structured record of each annotated document, capturing all extracted data, annotation responses, and status updates for streamlined compliance and audit trails
- Intuitive error handling – Smart checks identify and notify users of processing errors, making sure each annotation is complete and accurate.
The following diagram illustrates the architecture using AWS services.
The workflow starts with user authentication and authorization (steps 1-3). After those steps are complete, the workflow consists of the following steps:
- Users upload supporting documents that provide audit evidence into a secure Amazon Simple Storage Service (Amazon S3) bucket.
- The input documents are encrypted by Amazon S3 when consumed by Amazon Textract.
- Amazon Textract (encrypts data in transit and at rest) extracts the data from the documents.
- When complete, raw data is stored into an encrypted S3 bucket.
- Data sanitization workflow kicks off using AWS Step Functions consisting of AWS Lambda functions.
- Sanitized extracted data is written into an encrypted MongoDB.
- Amazon Textract is polled to update the job status and written into Mongo DB.
- The user starts the annotation process.
- Application logic consumes data from Mongo DB and provides it to Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock.
- The LLM runs the audit rules (shown in the following screenshot) against the extracted data and generates an annotation for each audit rule, including pass/fail details of the audit rule.
- Annotation results are filtered using Amazon Bedrock Guardrails to enhance content safety and privacy in generative AI applications.
FloQast AI Annotations offers the following benefits:
- Seamless integration with FloQast – This feature is integrated into the FloQast platform, providing access to annotation tools alongside your existing compliance and financial workflows
- Enhanced efficiency with AI-driven workflows – FloQast’s annotation feature uses AI to reduce manual workload, helping teams focus on high-value tasks rather than repetitive document review
- Scalable solution for high-volume document processing – Designed to handle substantial document volumes, FloQast AI Annotations adapts to the demands of growing teams and complex audit requirements
- Real-time document processing insights – You can stay informed with live tracking of each annotation job, with built-in monitoring for smooth and efficient workflows
FloQast’s AI technology choices
FloQast selected Amazon Bedrock because of its unmatched versatility, feature sets, and the robust suite of scalable AI models from top-tier providers like Anthropic. Anthropic’s Claude 3.5 Sonnet provides the advanced reasoning and contextual understanding necessary for handling complex financial workflows. However, a key feature of Amazon Bedrock—Amazon Bedrock Agents—is a game changer for FloQast. Amazon Bedrock Agents enables generative AI applications to run multi-step tasks across company systems and data sources. To learn more, see How Amazon Bedrock Agents works.
Amazon Bedrock Agents provides an intelligent orchestration layer, allowing FloQast to automate accounting workflows efficiently. It has added significant value in the following areas:
- Instruction handling and task automation – Amazon Bedrock Agents enables FloQast to submit natural language instructions that the AI interprets and executes autonomously.
- Session and memory management session – Attributes and
promptSessionAttributesare passed between sessions related to a single workflow, but most user requests can be singular to a session. - Code generation that demonstrates business understanding – Amazon Bedrock Agents offers valuable features through its secure code interpretation capabilities and flexible configuration options. Amazon Bedrock agents can be tailored to the correct persona and business context, while operating within a protected test environment. This allows accountants to submit natural language instructions and input data, which is then processed in a controlled manner that aligns with security best practices. When FloQast integrates with Amazon Bedrock Agents, accountants can submit custom requests, and the agent can generate and test code within an isolated secure environment, with appropriate technical oversight and guardrails in place. The combination of Amazon Bedrock Agents’ secure code interpretation features and FloQast’s deep knowledge of accounting practices enables financial teams to operate efficiently while maintaining proper controls.
- Data integration and output handling – By using Amazon Bedrock Agents, information is passed from upstream integrated financial systems, allowing FloQast to automate data retrieval and transformation tasks.
- Multi-step task orchestration – Amazon Bedrock agents are designed to handle multi-step tasks by orchestrating complex workflows. For example, after FloQast retrieves data from a financial system, that data is passed to the agent, which runs the necessary calculations, generates the output code, and presents the results for user approval—all in one automated process. This orchestration is especially useful in accounting, where multiple steps must be completed in the correct sequence to maintain compliance and accuracy.
The flexibility of Amazon Bedrock Agents to manage these tasks and integrate them seamlessly into existing workflows enables FloQast to achieve scale, reduce complexity, and implement automation required to cater to the evolving needs of FloQast’s customers.
Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock provides the best results in FloQast’s evaluation of other models for the use case. FloQast doesn’t need to fine-tune the model as a model consumer, so they use Retrieval Augmented Generation (RAG) with few-shot classification on data collected on the user’s behalf, removing the overhead of fine-tuning an LLM. For this use case, this design mechanism produces a higher level of accuracy, a better security model that is understood by FloQast’s customers, and ease of use as a developer.
Conclusion
FloQast’s AI-powered accounting transformation solution has had a substantial impact on its users. By automating routine, time-consuming accounting processes, the solution has saved accounting teams countless hours, enabling them to shift away from manual spreadsheet work and focus on higher-value activities, such as reviewing financial outcomes, assessing business health, and making data-driven decisions. This solution has removed the tedium of data reconciliation, delivering measurable improvements, including a 38% reduction in reconciliation time, a 23% decrease in audit process duration and discrepancies, and a 44% improvement in workload management.
Learn more about the FloQast platform at FloQast.com. Contact evelyn.cantu@floqast.com for more information about the FloQast and AWS partnership.
About the authors
Kartik Bhatnagar is a data security-focused Solutions Architect at AWS, based in San Francisco, CA. He has experience working with startups and enterprises across the tech, fintech, healthcare, and media & entertainment industries, in roles including DevOps Engineer and Systems Architect. In his current role, he partners with AWS customers to design and implement scalable, secure, and cost-effective solutions on the AWS platform. Outside of work, he enjoys playing cricket and tennis, food hopping, and hiking.
Aidan Anderson is a dynamic technology leader with over a decade of experience in software engineering, security, and artificial intelligence. Currently serving as the Director of AI Engineering at FloQast, he is at the forefront of integrating AI and automation into accounting workflows, enhancing operational efficiency and accuracy for finance teams. Aidan’s portfolio spans leadership across security, product development, and platform engineering – where he’s consistently driven innovation, built high-performing teams, and delivered impactful solutions in fast-paced startup environments.
