By Scott Parker, Director of Product Marketing, Sinequa
If last year was any indication of what financial markets will look like in 2019, we are in for a very bumpy ride. Last December alone, the Dow Jones Industrial Average fell and rose more than 8 percent as finance experts struggled to make heads or tails of a bizarre political climate, unsteady interest rates and global tariffs.
In the financial services community, turbulence and uncertainty drive change. And change produces data – mountains of it from acquisitions, portfolio adjustments, credit/debit card histories, customer inquiries, call logs and online interactions all stored and shared across hundreds of thousands of databases and data centers around the world. It’s estimated that U.S. banks on a normal day store more than one exabyte of data.
Banks and other financial services institutions are mining that data to improve customer relationships and avoid being caught flatfooted from seismic events in the market. The successful ones are evolving from being “data-driven” to “information-driven.” And yes, there’s a difference.
Using data alone does not necessarily lead to better decisions. The key to being information-driven is finding and extracting the right data that’s relevant to your business goals and customer interactions, and then connecting that data along topical lines to create unified views of customers, investments and companies. According to Forrester Research, organizations that use data more effectively to gain business insights are more competitive and grow more than 30 percent each year.
Information-driven data analytics leverages data mining, natural language processing (NLP) and machine learning to process massive data sets to find patterns that humans could never detect and help reduce risk. An information-driven approach to data analytics can help financial institutions stay ahead of major changes in the market and keep customers happy and loyal.
Simplify Regulatory Compliance
Regulatory compliance places a significant burden on financial services companies to find and produce data from troves of documents and records. Information-driven data analytics uses machine learning and NLP to classify content by identifying similar data sets and grouping them for faster retrieval and accurate interpretation. Algorithms that match specific regulatory requirements can also be used to classify the content to the right regulations.
For example, systems can be trained to automatically classify email and documents with the right confidentiality level to help prevent critical information from being lost or leaked. Machine learning can also be combined with algorithms to identify patterns that help recognize medical records, social security and credit card numbers, as well as other forms of personal information to adhere to policies like GLBA, SOX, GDPR and the new California Consumer Privacy Act.
Improve Insights for Researchers and Portfolio Managers
Research analysts and portfolio managers must evaluate data quickly and accurately. For them, being information-driven means reducing time spent digging through data sources and making trading decisions as quickly as possible based on real-time market data. Using NLP, researchers and portfolio managers can uncover patterns that frequently go unnoticed.
An information-driven approach for research analysts reduces the time they spend digging through data sources and enables them to quickly navigate and find critical market-moving events across both internal and external financial content sources such as fundraising, credits, litigations, roadmaps, and divestitures. With these insights, researchers can consistently make better decisions at scale.
For portfolio managers, an information-driven approach enables faster, more informed investment and trading decisions based on real-time market data such as unified views across assets with a timeline displaying important events. The information and insights incorporate content from subscription services as well as internal business applications and legacy systems.
Reduce Customer Turnover at Retail Banks
Retail banks rely on customers to be happy and loyal, which drives the need to reduce friction at every point of the customer experience. Every customer interaction produces a history or information that quickly accumulates into volumes of big data that must be automated and analyzed for a complete 360-view of the customer.
When banks have hundreds or thousands of internal users, it’s easy for insights to become siloed. Loan officers might have one view of the customer while the teller might have an entirely different perspective. This inconsistent view creates friction when customers have to respond to offers that don’t meet their needs or when they have to re-explain their situation to multiple bank employees. At the same time, customer might not be fully aware of the bank’s services that apply best to their financial situation.
An information-driven approach connects both employees and customers to match them with the right products and services. This means combining and analyzing data such as customer transactions, account balances, age, marital status, retirement and college investments, and loans for a complete look at their full financial situation. Then the proper products and services can be placed in front of them for consideration.
Also part of that 360-degree view are the external factors that affect customer actions and overall considerations such as stock exchange data, corporate websites, financial and trading news feeds. With this complete picture, customer service representatives can serve customers in a manner that meets their individual needs.
With a climate as unpredictable and volatile as today’s financial markets, it’s easy to get lost or sidetracked in a sea of partial and irrelevant information. An information-driven approach to financial services, on the other hand, creates the shortest and most efficient path to information that is relevant to customers and contextual to their situation.