How to Use Decision Intelligence to Improve Risk Management, Customer Experience, and Fraud Detection

by Ajay Khanna

Data in all its facets has dramatically impacted financial organizations in the last two years, both positively and negatively. Whilst rich pools of data have allowed for exciting businesses opportunities, it has simultaneously tasked business with extracting valuable insights with traditional Business Intelligence platforms.

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The amount and types of data financial organizations have access to has grown dramatically in the last two years. While this rich pool of data should present incredible business opportunities, as the complexity of data has grown, extracting valuable insights with traditional business intelligence platforms (BI) and self-service BI has become even more difficult. As new customers and opportunities present themselves within the ever-growing market of finance and fintech, it’s becoming increasingly important to harness all the data available at the company’s fingertips. 

Oftentimes, data analysis leaves businesses with the choice to either sacrifice the quality of their insights or to sink large amounts of time into data preparation, analysis, and model building to get the in-depth, granular information needed to inform decisions. To help extract these deeper insights, financial organizations should consider how decision intelligence can deliver a new level of data productivity, accessibility, and transparency 

Decision intelligence is a different approach to analytics that connects various data sources together and applies analytical and machine learning automation to give a complete picture of what's happening in the business, why metrics are changing, and how to drive business outcomes in granular ways. Decision intelligence puts greater analytical power directly in the hands of analysts and business teams and helps financial institutions gain advantages in three ways: by enhancing risk management, boosting the overall customer experience, and increasing fraud protection. 

Enhance risk management 

According to a 2021 global study by the Enterprise Risk Management Initiative, which surveyed more than 700 finance executives about how their organizations approach risk oversight, 60% or more of respondents said that the volume and complexity of risks has increased compared to previous years. Increasing “risk intelligence” starts by clearly defining, understanding, and managing a company’s tolerance for and exposure to risk. 

Traditional risk management approaches are no longer the optimal way of dealing with the emerging risk landscape because it excludes key factors--such as proactive risk prediction, uninsurable risks and understanding the relationship between different types of risks--that are only present within complex data. Organizations can improve risk intelligence by increasing visibility in key risk areas like operations, regulatory compliance, finance, ecommerce, and credit. 

With the emergence of big data and AI-driven data analysis, it is possible to integrate the internal and external data points for identifying emerging risks. These data points can be used to create analytical models to detect potential risks, fully assess their financial impact, and create an analytic framework that can begin to balance the strategic impact against the investment. Decision intelligence can enable decision makers across the company to measure, quantify, and predict risk with this data to create a consistent methodology steeped in data-driven insights. The technology can place advanced insights into the hands of analysts and business users—helping accelerate formative decision making across the entire organization and eliminating silos between data and sales departments. 

For example, a top 10 financial services firm used decision intelligence to improve credit risk analysis. The enhanced analytics capabilities streamlined to a single tool--versus the several solutions the company previously deployed--made it easier for their business and data teams to better collaborate, generate and test hypotheses, and put those into action. The result was thousands of hours of productivity gains, as well as $700,000 saved per month in mitigated losses in just one credit department.    

Boost customer experience 

Financial companies can improve customer engagement across transactional, marketing, and customer data sources by personalizing new offerings. By automating analysis of this customer data with decision intelligence, organizations not only get faster insights, but can also accelerate the cycle of experimenting on new ideas and testing new hypotheses that improve marketing campaigns and customer experiences. 

With decision intelligence, organizations can gain new customer insights that help drive acquisitions and improve customer lifetime value. By deriving insights that deliver the right offering to the right customer in the right channel, companies can stay ahead of the competition and retain loyal customers. With a single actionable view of customer relationships, decision intelligence leverages predictive analytics to understand customer behavior and discover customer financial patterns from real data. 

Decision intelligence can also measure performance, risk, style, and characteristics for multiple portfolios and asset classes to help financial service organizations better service their clients. Companies can use decision intelligence to help their clients understand composition and risk, view metrics (including weights, valuation measures, ratings, and other ratios for their portfolio and benchmarks) and evaluate relative performance using different attribution models. 

Increase fraud detection 

LexisNexis calculates that the cost of fraud for U.S. financial services and lending firms has increased between 6.7% and 9.9% compared with before the pandemic. This means that every $1 of fraud loss now costs U.S. financial services firms $4.00, compared to $3.25 in 2019 and $3.64 in 2020. 

Identifying fraudulent activity is a necessity given the growing sophistication of fraudulent methods and the need for financial services firms to maintain consumer confidence in their products and services. Applied machine learning, statistical analysis, and AI-driven automation can be used to benchmark user behavior, evaluate incoming transactions in real-time, and prevent losses before they occur. Building machine learning models from transactional data of even the largest datasets can help businesses improve their fraud detection capabilities. 

With decision intelligence, any potential fraud can be detected by spotting anomalies or deviations from “normal” behavior or patterns right as the transactions occur. Normal audit procedures, for example, often rely on sampling. This leaves substantial amounts of data unexamined or examined after the fraud has occurred. Decision intelligence establishes a baseline of data of non-fraudulent activity to compare to the suspicious dataset. It may also be possible to identify data known to be associated with fraud. This allows users to be one step ahead of any fraudulent activity, saving financial companies, and their clients, time, and money. 

Modern financial institutions thrive off using data to efficiently capture new opportunities, manage risk, and strengthen customer relationships. But with so much data at their disposal--and too many tools to store, access, and analyze that data--it can be challenging to turn insights into informed business decisions. Financial organizations deserve an analytics experience that delivers a new level of self-service and productivity, eliminating the bottlenecks of traditional analytics tools and making it possible for every user to leverage data. With decision intelligence, organizations can get deeper insights faster, enabling decision-making that will help them to surpass the competition. 

Ajay Khanna is the Founder and CEO of Tellius.