Data analysts can do so much more than just generate reports. Do your analysts have what it takes to be a major asset for financial services business leaders charged with crafting strategy? Here’s how analysts equipped with the latest AI-powered analytics tools are feeding decision-makers the insight they need, so they can make the right calls.

Strengthening collaboration and communication between data analysts and strategy leaders in financial services 

AI-driven technology is opening huge doors in advanced data analytics. With AI to help manage the quantity and sources, all relevant data streams (proprietary, public, third-party) can be efficiently brought into a single analysis. AI also has the ability to transparently guide analysts to insights, putting more knowledge in their hands.

With that additional knowledge, analysts can help break down information silos that exist between departments, roles, and corporate divisions. Imagine a data platform that takes historical sales spreadsheets, order forecasts from business development, expense projections from accounting, sales rep CRM notes, and so on, synthesizes them in one place, and identifies points of interest and insight. With transparent insight and clear visuals, everyone starts seeing business problems holistically. This fosters organic, cross-functional partnerships among people who may not have had full visibility into one another’s information or pain points before.

There’s power—and profitability—in the ability to provide access to overarching insight by exploring multiple data sources. Let’s consider just a few ways that advanced data analytics tools can make analysts the partners that financial business leaders are looking for.

Advanced analytics tools can demystify customer segmentation

True customer analysis requires collaboration from data, business development, marketing, sales, information security, risk management, ESG, and legal teams.

The data streams to be evaluated might include third-party and public data like credit bureau information, customer reviews, government statistical databases, social media signals, and economic indicator forecasts. Then there is proprietary CRM data from surveys, purchase history, and sales conversations.

With AI-powered advanced analytics tools, all these data sources can be brought into a unified analysis. Users can see how many dimensions interact in easy-to-understand, 3D data visualizations, instead of comparing a few customer attributes at a time. Customer segmentation becomes far more multidimensional, granular, and beneficial when data analysts can organize prospects into segments and plot them in network graphs while investigating the relationships between data including:

  • Demographics (age, gender, occupation, education level, marital status, and geographic location).
  • Psychographics (customers’ attitudes, beliefs, lifestyles, interests, and values, from surveys, interviews, and analysis of customer interactions and engagement).
  • Financial data (income, spending habits, saving patterns, investment preferences, and credit history).
  • Transactional data (purchase history, account balances, transaction frequency, and product usage patterns).
  • Channel behavior (online banking, mobile apps, customer service calls, branch visits, engagement frequency, service inquiries, complaints).
  • Risk preferences (investment goals, time horizons, past investment experiences, risk aversion levels).
  • Past and upcoming life events (marriage, family births/deaths, retirement, home purchase, promotions).

With tools like the Virtualitics AI Platform, data analysts can intelligently explore what-ifs alongside those on the front lines of business strategy. AI automatically pulls forward what is statistically significant. Teams can use natural language queries to investigate further, change up their visualizations, and dive deep into their data to find the real causes behind customer churn.

Want to learn more about understanding customer behavior? Check out our webinar to dive into real customer churn examples.

Create a clear narrative to explain complex market analyses

Market analysis is another workflow that benefits from Intelligent Exploration of vast, interrelated financial services data sets.

Say a data analyst is tasked with evaluating the six-month outlook for market conditions and fluctuations to identify potential impact on investment portfolios. This could include assessing risks related to interest rates, commodity prices, exchange rates, and other variables like climate change disruptions that affect the value of assets or new investments. Coordination with finance, strategic planning, the head economist, risk managers, and others is necessary.

The data to support this analysis might include economic indicators, historical stock exchange behavior, tweets from influential economists, statements of regulators, government jobs reports, and more. Analysis can quickly go deep into the weeds, becoming difficult to explain to the non-expert. AI-guided data exploration shares analyses in logical narratives and intuitive visualizations automatically, so that everyone involved in decision-making can understand the insight present. The data analyst is able to get all contributors on the same page and leaders have the knowledge they need to set the direction for investment strategies.

Dig deep into data to understand insider threats

Managing insider threats takes coordination across many roles, from HR to risk management to infosec to compliance. It also requires analyzing a lot of big data sources to suss out patterns and correlations: employee behavior monitoring systems, audit trails, access controls, performance reviews, and more.

Data analysts can use AI-guided intelligent exploration to define different communities within the workforce, find commonalities among employees with anomalous behavior, and start to assess the difference between unintentional mistakes and true red flags.

Network extraction technology allows an analyst to create a 3D visual representation of the people and factors that contribute to insider risk. All kinds of dimensions can be graphed: unexpected login hours, large file transfers, length of employment, and hours of cybersecurity training completed.

This information can guide decision-makers on how to enhance risk management strategies, adapt internal controls, and improve operational efficiency. Maybe there’s so much friction in remote desktop access controls for hybrid workers that authentication protocols need an update. Maybe more training on data security is needed due to upticks in spear phishing by bad actors. Network graphs coupled with AI can visualize at-risk communities, then AI can be queried to offer recommendations on what to do next.

Give data analysts the tools to get more value from financial data

Sixty percent of data science and analytics leaders in a recent CIO.com survey say their organizations’ data is not being used to the fullest, and more than three-quarters of respondents are prioritizing upskilling analysts to do advanced analysis. AI-powered advanced analytics tools allow organizations to do just that.

Are you ready to improve information sharing between data analytics teams and business leaders? The ability to consider all the relevant data and present findings in ways that are clear, concise, and regulatory-compliant is within reach. Download our new e-book to learn how to unleash the power of your financial data analysts.

Financial services organizations are sitting on more valuable data than they know what to do with…or manage and explore. Traditional analytics tools, like BI dashboards and 2D graphs, just aren’t up to the task of clearly explaining all that’s going on within their complex datasets. With stiff and growing competition from fintech, neobanks, and other new entrants, it’s no longer enough for those in financial services to use data analysis just to understand past performance. Strategic advantage, and maybe even survival, depends on companies adopting solutions that allow them to react in real-time and hone in on future customer and user behavior.

Intelligent Exploration is our AI-driven and AI-guided solution to data analysis that empowers analysts to overcome the obstacles in the way of success. Let’s consider how Intelligent Exploration could help teams address some common pain points in financial services.

1. Intelligent Exploration helps teams detect and counter fraud fast

Debit card skimming increased by 368% in 2022 year over year, at a cost of $1B to everyone from food stamp recipients to global banking institutions. Thieves are resourceful innovators, so this type of fraud will likely continue to grow.  

Banks, credit monitors, credit card companies, and others can use Intelligent Exploration of big data to rapidly detect card shimming and skimming. Say a credit card issuer wants to detect and predict risk factors. By exploring and analyzing their enormous, dynamic, complex data lake our AI algorithms can identify subtle, recurring patterns and define communities that humans can’t spot due to scale.

Cardholders might be grouped into segments based on geolocation, the type of merchants they visit, education level, or demographics. Or, analysts could look at the problem through a transactional lens, at dimensions like time of day, type of device used to make a purchase, amount, and typical purchase frequency.

Intelligent Exploration improves on existing fraud monitoring methods by making these patterns visible and clear in intuitive, data visualizations (such as Knowledge Graphs and 3D network graphs) so that teams can consider preventative actions that may be taken. Analysts can also leverage AI to frame questions in plain language, allowing AI to do the searching without any external factors (like bias) that could skew findings.

A query could be “What drives card skimming?” to produce a ranked list of the factors driving whether or not a merchant will be targeted with a skimming device. Outputs can be automatically packaged into illustrated, clear reports that tell the story of the risks—and point decision-makers in the direction of the most effective anti-fraud efforts.

2. Detecting and plugging data breaches faster

The average time to identify and contain a data breach is 277 days according to IBM, and the cost of data blindness can be very painful: Statista estimates the average cost of a data breach in the financial industry worldwide is nearly $6M. But Intelligent Exploration using AI to search through complex data stands to dramatically shorten this cycle, thus reducing cost and risk.

AI is really good at picking up on anomalies that exist in large data streams from different sources, helping analysts quickly pinpoint the root cause of a security breach. Explainable AI can take outputs and insights, including recommendations on next-best actions, and deliver them in intuitive graphics and clear text that build stakeholder confidence and speed up decision-making.

It’s also a big plus in helping organizations keep current on emerging threats. This requires continuous analysis of multivariate, external data sources (social media, threat intelligence feeds, public forums), a true flood of data. The insights are in there somewhere. Intelligent exploration lets analysts find and graph them, making them actionable.

3. Improving client retention in financial services

Today, it’s seamless and convenient experiences or bust for financial services companies. Customers expect this whether they’re making payments, applying for a loan, or opening a bank account. AI-guided data exploration can provide vital intelligence on customer behavior, needs, and preferences and then help decision makers prioritize how to respond.

For instance, a financial advisory firm could track and report on sustainability initiatives that are of growing importance for younger customers who want to do business with purpose-driven companies. Or, financial firms could jump on new product opportunities quickly with predictive analytics, define highly accurate customer segments, identify sales leads or best marketing channels, or discover how to improve a process that is causing customer dissatisfaction.

AI data exploration means competitive advantages for financial services

Competition is fierce in the data-intensive financial sector. Payment processors like PayPal and Square and options from big tech companies (Apple Pay, Google Pay, Amazon Pay) are encroaching on the online payment business, allowing customers to send and receive payments outside of traditional bank accounts. Digital-only banks with low overhead can offer higher interest rates on savings accounts and lower fees. Peer-to-peer lending platforms like LendingClub and Prosper let borrowers bypass traditional banks. Even the relationship-heavy investment advice and management business isn’t immune to all the disruption, with automation getting traction.  

Agility and innovation are required. Intelligent exploration with AI can help financial services firms meet the moment. Such cloud-based data analysis can integrate with legacy systems. It allows analysts to corral all the data coming at them, assess its relevance, and report on it in ways that make findings clear. For better decisions that improve financial companies’ competitive position.

Learn more about AI-driven data analysis in our Intelligent Exploration E-Book

 

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.

See how we use Intelligent Exploration to unravel the mystery of Customer Churn 

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.