With the collapse of a third bank this year, and an increasingly volatile financial market, financial institutions don’t have a lot of room for error. What they do have is a lot of data, both internal and external, and data analysts doing their best to find signal to guide them in massive, complex datasets. When analysts can truly explore all the relevant data, and visualize it in constructive ways, they can guide teams to winning strategies. But that’s not a simple task.
Data analysts in the financial industry need all the help they can get to discover meaningful insight in big data. With so many variables in the equation, minimizing the scope of data exploration introduces too much risk that the important findings will be overlooked. A dashboard or 2D graph isn’t telling the full story, and analysts are capable of so much more if they have the right tools to help them.
AI makes a powerful partner in big data exploration, bringing financial data analysts the guidance they need to explore their data and find valuable insight. We call this Intelligent Exploration: AI-driven and AI-guided data exploration that helps analysts avoid bias, find relevant connections, and do more with the data you’re already gathering. Intelligent Exploration uses AI to transparently illustrate the communities and relationships in your data and explains them so that analysts can communicate findings effectively.
You already have the data you need. Are you ready to get more value out of that data? Here’s what Intelligent Exploration can do for financial analysts, leaders, and strategists.
Analyzing Data to Get Ahead of Risk Management
Managing risk is one of the most massive challenges that financial institutions have to overcome. There is a wealth of information in financial data that helps teams identify, assess, and manage risks more effectively…if they can find it.
An analyst empowered with the right data exploration tools can assess the risk associated with investments, loans, credit scores, and mortgage default detection. Exploration of a rich set of data can identify patterns and trends that can help to predict the level and likelihood of future risks. This insight allows institutions to make informed decisions about where to invest their money or which loans to approve, while also being more aware of which customers are more likely to default on their mortgages.
What Your Data Already Knows About Financial Fraud
The concept of robbing a bank is one of the oldest jokes around, but the attempts at fraud against financial institutions have grown in both frequency and sophistication. They now pose a continual threat, always evolving their tactics, and it takes constant vigilance to fend off that risk.
Financial institutions must take proactive steps to detect and prevent fraud. Data holds patterns and anomalies in financial transactions that may indicate fraudulent activity, provided that analysts are able to detect those trends. With the right tools, data analysts can detect insider threats, expose behavior indicative of nefarious activities like money laundering, identify institutional weak spots, and see where authentication issues are occurring. Data exploration can also guide strategies like finding the next best action, enable enhanced due diligence, and make the reconciliation of receivables more efficient and accurate.
Provide Exceptional Service by Understanding Your Customers
There is plenty to be learned about customer behavior, preferences, and personas from customer data. AI-powered exploration that leverages network graphs can give analysts the tools to create detailed customer segmentation that help marketing and customer retention teams serve current customers and find new ones.
With the introduction of ChatGPT and Google Bard, we are seeing more and more people growing comfortable using chatbot-esque tools to interact with data. But financial organizations carry an extra burden of transparency and accuracy; bots must be trained to provide valuable feedback and accurate, defensible, and verifiable information to their customers. To offer this, institutions must invest in analytic tools that can visualize the recommendations generated by AI.
We’re also finding that younger financial clients want to know more about their investments, and not just about the money—they want to understand the social and environmental impacts, too. Customer conversations can show banks and lenders what their customers are looking for in their service experience, and by understanding that data, financial institutions can offer more personalized services and improve customer satisfaction. Solutions that enable banks to turn rich text into insight that can be analyzed alongside numerical and categorical data are invaluable.
How Intelligent Exploration guides financial companies to powerful answers
Empowering analysts to discover rich insights is just the beginning. For your entire organization to benefit from the enhanced exploration capacity of the analysts, they need to be able to share that insight with stakeholders and determine the next steps to capitalize on those findings. Whether they find an ‘aha’ moment of insight that needs to get in front of leadership, an important metric that frontline workers should have in hand as they execute daily tasks or a potential opportunity for an AI use case like automated monitoring, analysts who can do deep exploration have the power to move the business forward.
Financial institutions should expect accurate, insightful guidance, and clear understanding from their data analytics tools—because we already know that’s what customers expect from them. Schedule a 1:1 demonstration to see how the Virtualitics AI Platform puts Intelligent Exploration into the hands of data analysts or learn more about Intelligent Exploration in our free e-book.