By: David Guthrie, CTO, REPAY
It’s no secret that exploring, adopting and leveraging AI technology has become the primary focus for many businesses in recent years. The much-discussed promise of AI’s potential to drive productivity and business value has led many businesses in various industries to adopt the technology for many uses, from administrative tasks to highly specific workflow applications. This trend is proven by a McKinsey report that found 65% of businesses regularly use AI in at least one business process – a two-fold increase from just one year ago.
While the finance industry is equally eager to adopt AI, with 66% of leaders expecting AI to have an immediate impact, challenges around data preparation, security and compliance are still major obstacles on the path to widespread adoption of AI. This is an especially challenging problem for financial institutions, credit unions and banks with smaller workforces and fewer resources to draw upon for data preparation and AI exploration. Financial technology (FinTech) solutions that automate the processing, tracking and accounting of financial payment data can serve as a vital resource in the AI race by helping to prepare and cleanse financial data for ongoing and future AI analysis.
The Current State of Financial Data
Paper-based accounting and payments are rapidly becoming a thing of the past, but some financial institutions still rely on paper and manual processes to submit and accept payments. This leads to opportunities for human error when payment data is manually transferred to accounting systems. When accounting systems automate the input of payment data by scanning paper checks, there is still a risk of a misread number, name or other payment detail if handwriting is difficult to read.
Spreadsheet-based accounting and payment tracking, while more organized and navigable than paper-based processes of the past, still present problems for AI analysis. Financial data, due to security and regulatory compliance concerns, is also often stored in bespoke data repositories. This disjointed, unorganized data is unfit for machine consumption and AI, which requires cleansed and labeled information for accurate training and analysis.
Financial data is often pulled from multiple sources in various formats, requiring standardization, complex integration and normalization to prepare it for automated analysis. If different departments within financial institutions leverage multiple separate data silos to store information, this can add further complexity to data aggregation and normalization. To generate an accurate AI-driven analysis, financial data needs to be complete, accurate and standardized.
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The Role of Automated Payment Solutions for AI Analysis & Training
Automated payment solutions that can process, track and account for financial data not only provide an easier, simpler way of managing and accepting payments, they also provide a smoother path to AI exploration, training and adoption. This is because automation can provide businesses with highly detailed, accurate and usable data that can be used to generate valuable insights. Modern digital payment systems can instantly process and record vast amounts of transaction data, including dates, cash amounts, recipients, expense categories and other details as defined by the financial institution.
Automation speeds up the tracking and recording of financial information, while also removing the risk of human error, and immediately cleanses the data for machine readability and AI analysis. Once a digital payment is submitted, the automated solution processes the payment and instantly adds the details to a data repository. How this data is stored can be determined by the financial institution to provide a uniform structure and format that lends itself to efficient and accurate analysis by AI-driven systems and solutions. This removes the issues of manual error when entering financial data, as well as the need to go back and cleanse data once it has been recorded – eliminating some of the more time-consuming obstacles on the path to AI enablement.
Once an organization deploys AI for one process or for a specific department of their business, they will undoubtedly recognize the near-immediate benefits provided by instant analysis and valuable AI-driven insights. Being able to scale AI solutions to handle larger volumes of information across wider operations is critical to the long-term success of AI deployment and advancing an organization’s place in the AI race. By using automated solutions to track and process data across payments, financial institutions can ensure their AI solutions can consume and analyze as much data as possible – in a machine-readable format that facilitates the expansion of AI analysis. Further, any new AI solutions that are adopted can be trained on the same dataset that was used initially, ensuring standardization and reducing bias across AI-generated insights because each solution is working off the same information.
Advantages of Automated FinTech Tools for Compliance & Security
Ensuring the security of private financial information, as well as compliance with financial regulations and standards, are some of the most critical obstacles to overcome on the journey to AI deployment. Leveraging automated payment technology provides a springboard for financial institutions, enabling them to effortlessly leap over these hurdles.
When payment information is processed by an automated solution, it never needs to be touched by human hands. In addition to negating the issue of human error, this also removes the risk of unintentional leaks of information through negligence or malintent. All information is instantly tracked in a data repository of the financial institution’s choosing, ensuring adherence to all security standards and regulations for every single piece of information recorded.
The Future of FinTech AI
Ensuring a successful future for almost any financial institution will soon require the use of AI-driven solutions to, among many other things, generate critical business insights and guide strategies for growth. How organizations prepare to enter and continue in the AI race, which is already well underway, as well as how they plan to scale AI solutions, will be the key to maintaining a competitive pace. No financial organization should feel held back by data challenges, security concerns or skepticism of AI’s accuracy, with the assistance of FinTech automation.
This race has no finish line. So, organizations must constantly adapt to changing technology landscapes and explore the potential of new solutions to remain competitive. Leveraging automated payment and FinTech solutions ensures that businesses are prepared to tackle any data-related obstacles – including data preparation and cleansing, information security and compliance challenges – and embrace the future of FinTech AI.
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