Artificial intelligence (AI) is becoming increasingly ubiquitous in our daily lives, from the recommendations we receive on social media to the autonomous vehicles being tested on our roads. Yet, for many people, AI remains shrouded in mystery, and the thought of interacting with it can be intimidating. However, there are several steps you can take to familiarize yourself with AI and gain a better understanding. First and foremost, it's essential to understand what AI is and how it works. At its core, AI is the use of computer algorithms to perform tasks that typically require humans, such as recognizing patterns, making decisions, and learning from experience.

If you've been keeping up with the news lately, you've probably noticed that AI is everywhere. From the concept of self-driving cars to newcomers like voice generation, deepfake videos, and OpenAI (Midjourney and ChatGPT), AI is changing the way we live and work. But it's not all sunshine and rainbows – there are also concerns about the ethical implications of AI, particularly when it comes to fraud. The first question we must ask ourselves is: why is AI a dangerous fraud trend in banking? AI has the power to automate and streamline banking processes, which can be exploited by fraudsters.

From restaurants to retail, industries where customer service is an integral part their business continue to deal with major staffing shortages. This includes financial services and financial institutions, and by extension, credit unions. Securing reliable and long-term frontline help devoted to providing member service was difficult even before the COVID outbreak. Following the tidal wave of closures of physical branches that accompanied pandemic lockdown, many member service representatives left for other positions, or simply never came back. With no end in sight to the staffing crunch, and members demanding a more personalized service experience, credit unions must consider an option many previously thought was too expensive, 'impersonal', and too complex to maintain.

ChatGPT is causing waves in just about every industry, and there's no doubt it will be a powerful tool in assisting roles that involve written and verbal communication. But will it replace a universal associate? Compare it to digital/mobile banking, and the benefits and drawbacks of the technology. While ChatGPT presents the possibility of efficiency and scaling, and there are security concerns and it's never going to completely replace your staff members and human interaction and unique problem-solving skills they bring to the table. Digital banking and ChatGPT are solutions for scaling.

In its most recent advisory, the CFPB addressed a critical question – "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken? The answer is an obvious'Yes'. With the CFPB's circular reminding everyone of adverse action notice requirements under the ECOA Act, some credit unions find themselves in a quandary when it comes to explaining their credit decisions, which is perceived to be difficult when they use state of the art decisioning algorithms. However, modern AI solutions have moved beyond mere aspects of explainability to enable fair lending, and have gone the extra mile to remove inherent biases that may arise in data based models. Nonetheless, it is necessary to understand the CFPB's guidance and how AI can effectively be a solution itself. The use of algorithms in making lending decisions is not something novel or new. Credit risk assessment naturally requires getting your arms around as much relevant data as you can. A mix of models and algorithms have been the backbone of credit decisions for around 4 decades now, with credit analysts using financial statements, credit histories, and other data sources to estimate credit risk, set credit limits and recommend payment plans. With time, the datasets in question have become so voluminous that lenders had to move from manual methodologies to computational models for analysis of data using analytics. Recent advancements in computational methods have introduced the "AI" element in lending processes to make credit risk assessments much more accurate. Artificial Intelligence and Machine Learning models leverage a diverse set of alternate data sources beyond bureau, and use historical training data to determine non-linear correlations between data points, and provide advanced predictive signals on member behavior and lending outcomes. The unique proposition here is the ability of AI/ML models to analyze voluminous quantities of data, detect hitherto unknown correlations, and keep self-learning and adapting the models with little or no manual interventions. AI enabled technologies have helped put the spotlight on the increasingly visible disparities in existing lending processes. A 2019 paper by Robert Bartlett & Co. helps quantify this disparity: "Black and Latino applicants receive higher rejection rates of 61% compared to 48% for other races.

Organizations are working relentlessly to make the customer experience at every touchpoint of a transaction as seamless as possible. And the conversation around AI has intrinsically correlated itself with intuitive, human-based customer experiences. The need for identifying and solving customer challenges intuitively and objectively while having access to all possible information pertaining to said needs is of utmost importance in the present economic climate. Pankaj Kulshreshtha, CEO at Scienaptic AI believes that financial institutions today may be ahead in incorporating the latest technology but there is a gap in how this technology translates into customer experience. Their systems of records (Bureau, databases, core, alt data) need to connect with systems of engagement (CRM, LOS, digital apps) and all of this needs to be powered by systems of intelligence i.e. decisioning systems for every touch point powered by AI. "We hear of instances where an individual with $700,000 of petty cash sitting in his bank account is being offered a $500 credit line. These are instances where despite the seeming intelligence of financial systems, they don't seem intuitive enough."

If there are kids in your life (or even some adults--we don't judge), you may have heard of the open world sandbox game Minecraft. You start with nothing--gathering some basic raw materials and finding food and shelter--but in order to really get ahead in your worlds, you need to level up your game. You have to figure out which elements to put together to create the things you need to not only survive but thrive. Today's risk decisioning is also about evolving beyond the basics. When you start out making credit risk decisions you may just have the essentials--some data, some workflow tools, some basic automation.

When Abound Credit Union first opened its doors in 1950 in Radcliff, Kentucky, its member base could fit in the confines of a small conference room. Back then, the credit union's main goal was to deliver deeply personalized service to its members in-branch. Fast forward 72 years and Abound is now Kentucky's largest credit union with nearly $2 billion in assets, 18 locations and over 100,000 members across central and southern Kentucky. Though its mission to deliver outstanding member service remains, much has changed about its members' preferences -- namely, for simpler and more efficient online banking services.

Artificial intelligence (AI) is not only the norm, but the expectation for just about every aspect of life, including banking. AI is an intelligent and virtual resource that deploys data to replace and improve business functions previously completed by human capital. In the world of banking, AI implementation is increasing revenue, mitigating risk, and improving the member experience through mobile banking. Chances are your branch will not get the same number of opportunities to impress members as you did two years ago. Once a member experiences 24/7 support with zero hold time, they will choose that convenience, and AI will allow you to provide that level of service.

On November 30, a panel discussion was conducted with a focus for credit unions addressing "Assessing risk and optimizing growth for each member", hosted by Neuton.AI. This event brought together thought leaders from the industry who shared views on how credit unions can uncover new growth opportunities and mitigate risks by leveraging AI. Needless to say, the pandemic has caused a seismic shift in how we interact with customers such as how members are now expecting to consume services, their digital expectations which have in turn forced credit unions to rethink the way they interact and respond to member needs. This has subsequently led more and more credit unions to adopt a more data-driven mindset while leveraging innovative technologies such as artificial intelligence or machine learning. Beginning this journey, institutions are faced with a number of challenges such as where you begin, why data is important, what the possibilities are, and how I complete this journey when I may not have the resource or financial capital that is historically required to implement such services.