Machine Learning in Finance

When tasked with writing about “Machine Learning in Finance,” my mind immediately reflected on the diverse projects I’ve been involved in. Each project has been incredibly engaging, from developing ChatBOTs to predicting the top 5 most used features by our users. However, this led me to ponder about AI. The next question was whether machine learning is a component of generative AI and its implications in the banking and finance industries.

So, before we delve deeper, let’s clarify the distinction between Machine Learning and AI. Machine learning hinges on utilizing data to forecast outcomes or make decisions, while AI is geared towards automating tasks that mirror or surpass “human skill.” Both AI and Machine Learning necessitate vast volumes of data for processing and learning. In essence, it is accurate to assert that machine learning falls within the AI domain.

When discussing AI in Finance, our audience’s reactions vary. Some are enthusiastic, while others adopt a more cautious “wait and see” attitude. This divergence stems from the belief that finance and AI may conflict regarding regulations, policies, and controls.

Banking and finance are integral to our economy’s stability, serving as the foundation for our financial well-being. These sectors prioritize trust and accountability, which are essential for the nation’s economic health. Despite facing challenges like stringent regulations, they remain vigilant in protecting against risks and fraud, ensuring a secure financial environment.

AI, on the other hand, is reshaping the banking industry, introducing innovative solutions, and enhancing efficiencies. However, work must still be done to regulate AI usage, strengthen privacy controls, and improve risk management strategies. The future holds promising advancements as we navigate this evolving landscape.

Many clients and businesses may not realize that AI and machine learning are not new concepts in the banking industry. And you don’t need to be a data scientist or physicist to tame and master these advancements. You don’t have to learn about neural nodes and the billions of connections required to create products that will streamline complex processes, reduce manual hours, and automate complex tasks.

Banks and FinTech have been leveraging these technologies for as early as two decades. My journey with machine learning began seven years ago when we explored implementing a ChatBot for our customer support tools, a SAAS product that had already been around for an additional five years.

 Research indicates that these newer generations are expected to receive generational wealth transfers from the previous generations. They are also less trusting of big brands, and although they may be financially prudent, they may not have relevant finance knowledge 

In my experience, Machine Learning offers a vast potential to enhance backend efficiencies and customer communication, surpassing its impact on front-end user experience. Nevertheless, it’s crucial to note that it can also significantly reduce friction in front-end capabilities and overall user experience.

More than five years ago, our team dedicated itself to inputting knowledge documents and data into the system, enabling our application to anticipate hints and answers for our banking clients. This initiative aimed to decrease call volume and enhance the speed of addressing customer queries. However, our efforts didn’t end there; we also analyzed the nature of incoming queries and compared them against customer profiles. This strategic move allowed us to identify the most pertinent concerns across different customer segments.

This could have been easily implemented in any industry. However, training our bot to analyze data efficiently and make critical distinctions was a pivotal challenge. We’ve focused on ensuring that information is appropriately categorized as pre-login or post-login within our online banking application. The stringent fraud prevention and risk mitigation measures significantly influence our system’s response to user queries.

We developed additional learning models utilizing Machine Learning to forecast the transactions and beneficiaries our clients will engage with.

These use cases led to targeted marketing campaigns for specific client needs and insights into emerging trends. Banks have even used machine learning to determine creditworthiness using data that may or may not be structured.

Machine learning and AI tools offer exciting possibilities to improve customer interactions and elevate employee contentment. Despite the potential benefits, navigating obstacles such as compliance with changing regulations and differentiating between genuine transactions, fraudulent behavior, and service disruptions can impact the effectiveness of learning models.

Let’s understand some scenarios:

Let’s say there is an online service interruption on a particular day while your associates feed the data and create a specific model. The system may simply assume that missed data during interruptions were a deliberate part of the data feed and, in the future, might skip taking data intermittently. This can have a severe impact if you rely on those tools that have incomplete data.

In another scenario, a change in law may determine how and what information is being fed into the system. The new laws may have made the existing data obsolete or illegal information to feed. For example, social media uses machine learning and algorithms to recommend certain specific content to its users. However, US lawmakers have recently questioned this model and may decide against it; your team will have to recreate the model to adhere to news laws, making years of data collection invalid.

Lastly, human interference can impact the results of machine learning models. Remember, data can be manipulated deliberately to minimize the solution’s efficacy.

Addressing these challenges is crucial to ensuring accurate conclusions and recommendations from AI-powered tools. While current limitations exist in understanding anomalies and disruptions, ongoing advancements in this field inspire confidence that today’s hurdles will soon become a thing of the past.

The younger generation is tech-savvy and relies on AI tools to complete tasks that require minimal attention. They already are or soon will be banking customers. Research indicates that these newer generations are expected to receive generational wealth transfers from the previous generations. They are also less trusting of big brands, and although they may be financially prudent, they may not have relevant finance knowledge. We must understand these challenges and opportunities to better serve our clients, new and old.

Machine learning is a crucial tool across industries. In the finance sector, where innovation, fraud prevention, and efficiency are paramount, leveraging this technology is essential to staying competitive. Embrace the power of machine learning to stay ahead of the curve.