How Analytics and AI Will Reshape the Future of Banking

Marc Andrews, VP Watson Financial Services Solutions, IBM [NYSE: IBM]

Marc Andrews, VP Watson Financial Services Solutions, IBM [NYSE: IBM]

There are many areas of focus for banking that have the opportunity to be dramatically reshaped by the application of more advanced analytics, including cognitive and Artificial Intelligence (AI). The two most prevalent ones are around generating an increased level of customer insight and improving the efficiency of understanding regulatory compliance requirements.

Today’s consumers are more in control of the buying process than ever before. In financial services, this means banks must adapt to their customer’s ever-changing needs, providing seamless, end-to-end personalized experiences. Like others in different industries, banks are now realizing that they are not innovating quickly enough to meet these demands.

The customer of today wants a bank that understands their needs, wants, and goals while simultaneously presenting them with personalized products and services. They also want their banks to advise and educate them, but prefer making choices based on a variety of options. Finally, they want to be engaged in real-time, with predictive alerts and tailored communication.

The way for banks to do this is by utilizing the wealth of customer data at their disposal to extract deep and valuable insights. The most high-performing banks are the ones using Big Data capabilities to assess their customers from more sources than competitors, while also using more advanced analytics to turn data into insight.

Prebuilt predictive analytics can help these banks extract deep customer insights from their existing data.

However, customer behavior is constantly changing, requiring more dynamic segmentation and analysis capabilities, which can only be addressed through more cognitive and machine learning based approaches. This allows banks to analyze transactions and spending patterns associated with specific life and financial events to better identify customer behaviors and understand the best way to act on them. Interactive and role-specific dashboards are also key to helping bank employees share these predictive insights among different teams, which ultimately leads to better business decisions at faster speeds.

Utilizing advanced models that are dynamic in nature means banks can create offers that are more relevant to the individual customer, based on anticipated life and financial events, recognizing that the customers have different reasons driving that event or activity. Banks can then provide appropriate actions in response to a crisis and proactively shape customer treatments based on anticipated spending and its financial impact. Banks that present more targeted solutions to their customers have seen significant improvement in response rates, which has resulted in increases in average deposit balances and reduced attrition.

Another focus for banks is keeping pace with the onslaught of recent legislative and regulatory changes. Since the financial crisis of 2008, there has been a sharp increase in enforcement actions brought by federal and state regulators in a broad range of cases involving financial and securities fraud, economic sanctions violations, money laundering, bribery, corruption, market manipulation, and tax evasion. In this lens, AI is a natural fit for the space because it can be used to tackle the significant amount of the analysis required to read and interpret complicated regulations.

Today, the traditional process of distilling regulations is a demanding and continuous undertaking. Compliance professionals must sort through hundreds of regulatory requirements and determine which lines of text apply specifically to their organization. This means that different staff can arrive at different conclusions, adding another layer of issues to an already complicated business process.

By using AI or a cognitive system which mimics how humans reason and process information, companies can completely transform key portions of their regulatory compliance activities. Cognitive systems analyze structured and unstructured data (in this instance, complex regulations), using natural language processing to understand grammar and context. At the same time, they also understand complex questions and present recommendations based on supporting evidence and the quality of information provided.

Finally, cognitive compliance means that companies can achieve a comprehensive view of regulatory compliance across all jurisdictions, business operations, and risk disciplines. Gone are the traditional status quo days of labor-intensive compliance processes, replaced with efficient and cost-effective change designed to transform a company’s compliance dynamic from reactive to proactive.