AI is enabling digital transformation across the financial services industry, from fintech and investment firms to commercial and retail banks. With the power of AI, financial enterprises can increase revenue, reduce operational costs and increase customer satisfaction. The combination of these benefits gives organizations that embrace AI a competitive advantage in the marketplace. In fact, recent findings show that 83% of business leaders believe that AI is important to their company’s future success.1
In my experience, my peers and I do whatever it takes to keep our customers happy. The world is becoming more customer-centric every day, and if there’s a tool that’ll make financial services folks’ jobs easier, we’re all ears. Customer expectations are higher than ever and they are asking for a highly personalized customer experience — AI is the perfect way to delivery it.
Better technology means better business models
Investing in AI grants businesses access to more accurate models, giving them a competitive advantage. Unlike older machine-learning practices and regression tools, new AI techniques can find complex patterns in data, even unstructured data such as text, speech, images and video. Now think about every business process that touches the customer — almost every one of those processes deals with data. By extracting more value from this data and improving efficiencies, financial organizations can realize the benefits of enterprise AI through enhanced applications and services that increase revenue and reduce costs.
Reduce operating costs and risk with AI automation
Embracing AI can also minimize manual processes. Automation is key in situations where decisions must be quick and accurate and are necessary to eliminate risk. For example, business leaders can reduce operating costs by using AI-enabled services, such as conversational AI, robotic process automation (RPA) and recommendation systems, to automate manually intensive tasks. These services can yield call transcription by augmenting call center agents, process and analyze digitized documents such as a loan or mortgage and more.
The finance industry is exposed to various types of risk because they have troves of data that need protection — it’s crucial that they manage this risk wisely. By using AI for behavioral analytics, which looks at what occurred in the past and analyzes current and predictive data to reach a conclusion, business leaders can identify risks such as cyberthreats or insider threats before they happen.
AI can also minimize credit risk by building machine-learning predictive models from customers’ data. With this technology, banks can assess the probability that a new applicant will default on a loan in the future and can avoid issuing loans to these individuals and minimize the default.
Further use cases for AI and risk management include fraud detection, insurance underwriting, forecasting market trends and making operational decisions.
The challenges of AI transformation in financial services
Embracing AI transformation is met with various challenges. For example, almost half of AI projects never make it to production. A big reason for this is that there’s a technology mismatch between the data scientists in the labs and the production systems that run the business. Data science tools have new releases and add new capabilities and performance boosts every month, so their software environments are quite complicated. On the other hand, production systems typically require more controlled processes and continuous development as opposed to being able to make updates on a daily basis. This leads to compatibility issues between data science and production infrastructure.
Another challenge of AI adoption stems from merging data silos in financial services companies. These organizations collect terabytes of data; however, it is often stored in several places. It’s imperative for financial institutions to be really prescriptive about how they integrate data silos and ensure the data is structured the right way to solve for a given use case.
Information security and regulatory compliance can also create a challenge for AI transformation. Financial services companies have very sensitive data and likely don’t want everyone in the organization to have access to that data. As a result, leaders must consider the organization’s comfort when deciding where to place workloads, where the data processing will take place, etc.
Don’t let these challenges scare you off from plunging into AI. The benefits truly outweigh the potential pitfalls. To combat these challenges and speed along the AI transformation journey, it’s important for financial leaders to take the time to map out how they will use these technologies and what use cases they hope to solve for. As new use cases emerge and AI scales across organizations, the next challenge for C-suite and IT leaders will be building out enterprise-level AI platforms that deliver the productivity, scalability and return on investment necessary to support AI teams across their companies.
To learn more about how to enable data science at scale in production and how AI is reshaping the finance industry, check out the VMworld session “Expand the Impact of AI in Financial Services.”