Banking analytics tools allow companies to create a clear picture of their entire institution. Using data analytics to avoid siloes and give all departments access to information improves customer service, internal communication and overall efficiency.
Legal departments can use data analytics to compare outside spending versus budget or IT systems within the department with peer banks. They can also look at year-over-year data for key performance indicators.
Risk Management
Banking analytics can identify fraud risks consistently and objectively, improving overall bank security. It also helps institutions avoid costly legal fines and penalties for regulatory non-compliance.
Financial institutions capture a wide range of data about their customers, loans and deposits. However, the information is often stored in silos and only accessible to certain departments or individuals. This creates a lack of transparency and miscommunication. Data analytics solutions can consolidate reports into a single dashboard to improve communication and efficiency.
The finance industry needs data analytics to provide the right services to their customers and employees. This will allow them to build trust with their customers and inspire loyalty. It will also help them meet business goals and manage operational challenges. In addition, they will be able to find new markets and design better products.
Customer Relationship Management
Banks must apply data analytics in customer relationship management (CRM), prospecting and onboarding, and client services. Historically, most corporate clients have been serviced by bank relationship managers who have relied on their understanding and interpersonal talent to nurture and deepen client relationships, drive new business and retain existing accounts.
Using digital tools to track and process information more rapidly can significantly reduce the time RMs spend on administrative tasks, allowing them to better engage with their clients. For example, digitized prompts based on analytics can automatically send RMs next-best-offer recommendations or flag accounts with shifting profile characteristics that require attention.
Likewise, digitized tools can improve onboarding by digitizing core onboarding journeys, including user-friendly mobile apps and behind-the-scenes processing.
Fraud Detection
Fraudulent activity in the banking industry can cost companies billions of dollars. Banks use data analytics to identify fraudulent patterns to protect customers and avoid costly lawsuits.
Data analytics uses machine learning to detect anomalies in financial transactions, images, and unstructured text. Banking litigation support helps organizations monitor fraud in real-time and prevent losses.
For example, data analytics can determine whether an individual is committing fraud by looking at the speed at which they type, move their cursor across the screen, and click the mouse. It can then compare this to a person’s average age rate. In addition, it can analyze and identify suspicious activities like atypical bank withdrawals or deposits. These can be flagged and reported to the bank. The resulting savings are enormous for the bank and its customers.
Product Development
Banks collect daily data from automatic teller machines, credit card purchases, and credit processing departments. This data is usually stored in different locations, making it difficult to consolidate and analyze. Banking analytics can streamline processes and improve efficiency by identifying patterns that lead to better decisions.
For example, one US bank used advanced analytics to reduce its teller transaction cycle time and boost customer satisfaction. Another bank used analytics to improve its dispute management process by separating complex disputes from simple ones and assigning them to analysts based on their skill sets and other arguments they’re working on.
Banks should understand that analytics is a team sport. It’s not enough to have a few people in the IT department who know little of everything. The best teams have true data science, engineering, and architecture experts.
Human Resources
Banks use data analytics to manage the huge amounts of data they generate. This helps them save time and resources by allowing employees to focus on more important tasks. For instance, a bank can use analytics to determine how much it costs them per teller transaction and then implement changes to improve the figure.
Bank customer data is rich with information that can help institutions detect new opportunities to attract customers and increase profitability. For example, a company can use data analytics to analyze its customers’ investments or shopping trends and target them with relevant marketing messages.
Banks can also use data analytics to respond to sensitive subpoenas and legal requests by identifying client relationships, accounts, and transactions and gathering documentation. This can significantly reduce the time and cost of responding to these lawful requests.
