By Sergio Suarez Jr, Founder and CEO of TackleAI

In recent years, the onset of Artificial Intelligence and Machine Learning has led to an increase in widespread technology automation. Simply put, automation technology enables processes to occur automatically, by predetermining criteria, relationships and related actions. Offloading redundant and simple tasks to computers is done with little to no human intervention –not only does this greatly reduce labor costs and time, but humans are freed up to use that time in more efficient and meaningful ways.

While just about every industry has felt the impact of technology automation, one of the most influenced will be banking and finance, because of the massive level of paperwork, data and analytics associated with the field. According to a Mordor Intelligence industry report, the global AI in the Fintech market was estimated at USD 7.91 billion in 2020, and it is expected to reach USD 26.67 billion by 2026.

A major development stemming from automation in banking is based in the quality of analytic research.Typically, analytics are used to determine trends – while this has been the status quo for years, one of the downfalls is that analytics are more akin to a ‘pattern finder’ and less of a ‘pattern understander’. With automation technology, the data is not only read and analyzed, but recognized in the context of patterns, leading to a great increase in the quality of automated decision-making. True Artificial Intelligence in automation will not only highlight a pattern, but explain why it exists, and why it matters. This is crucial to organizational decision making for current and projected trends.

Increased quality in automated analytics poses a variety of benefits in banking, specifically when determining approvals for loans. Data on the loan application can be analyzed, and compared to historical data for risk assessment and ultimately determine whether the loan is approved. This allows loans to be processed much faster, with less associated costs, and with less errors than a human would cause. Offices are typically inundated with paperwork that needs to be processed, which is time consuming for both applicants and loan officers. Automation technology allows the same documents to be read, analyzed, redacted, or classified in a seamless, fast way. Humans are given the leeway to spend their time on tasks that need humans to complete.

Automation will also extend further into personal banking and finance, through the apps, sites and digital tools we’re already accustomed to. Loan processes will become faster. Investing will be easier for people not considered “experts” because they will have the same access to the data the AI is giving that everyone else has. AI and automated technology make for a more level playing field when it comes to financial decisions, personally and on a company basis, allowing personal finance to become easier, more accurate and more beneficial for all involved.

The limitation of automation technology, and all artificial intelligence processes, is that the system learns by training data – and when data is faulty or biased, AI-driven automation may replicate these faults unless otherwise instructed.. For example, in 1988 a school in Britain was found guilty of discrimination. The computer program in use was inviting applicants for interviews, but was biased against women and those with non-European names. While the AI was matching the previous human admissions with 90-95% accuracy, the model it was training from was faulty, and so reiterated these biases. Unfortunately, the same could be done in banking and finance with loan approvals and mortgage rates. The AI trains on data you give it, but if the historical data the AI model is trained on has biases, it will match those biases with great accuracy, and thus reinforce the biases.

This makes it critical to adjust the data that the automation system is trained on. Historically biased or discriminatory data must be recognized before adding it to the system so it doesn’t reinforce it. This is why diversity in the workplace, specifically within banking and finance, is so important. Having people from a variety of different backgrounds helps recognize those biases in the data, thus training the AI so it gives more balanced decisions based on fair data.

Automation will unlock many doors in banking and finance, and likely sooner than we think. For banks, amongst many things, automation will boost capabilities in terms of trend analytics and measurement, and also expedite the approvals and processing for loan applications, which currently takes an inordinate amount of time and human effort. In personal finance, automation will broaden the common person’s accessibility to loans, investing, and informed banking decisions, heightening opportunities for long term financial planning and stability. So long as we’re conscious of the data and information we’re feeding financial AI models, and maintain integrity towards the potential biases that exist in historical financial data, automation will make banking faster, easier and more productive for all involved in the process.