Data intelligence helps facilitate action. Whether we want to admit or not, artificial intelligence is rapidly becoming a part of our day-to-day lives as consumers are routinely inundated with personalized solutions that adapt and respond to available data. Across the financial industry there has been a shift towards greater data consumption and digital transformation, accelerated further by the global coronavirus pandemic. We live in a data-driven society where artificial intelligence is emerging in the form of operational tools to help manage and organize the vast amounts of available data.
This blog explains what artificial intelligence is, the idea of robotics, available data tools, and how these resources are impacting lenders across the financial industry.
What is Artificial Intelligence?
Artificial intelligence (AI) is the development and deployment of systems that are able to perform human tasks, such as decision-making, predicting outcomes, assessing efficiencies, and automating adaptions to complete a specified task. There’s a spectrum of currently available artificial intelligences that range from integration to automation to machine learning and cognitive processing.
AI is being used widely across industries to help tackle challenges by providing personalized, data-driven solutions. This can include:
- Communication with consumers
- Customer support
- Marketing messaging and creatives
- Operation automation
- Predictability and analytics
- Sales and lead generation
What is Robotic Process Automation?
Robotics process automation (RPA) is one type of simplified, rules-based artificial intelligence that utilizes specialized computer programs, or software robots, to automate and standardize business processes. These “robots” mimic human actions by interacting with digital systems to execute a process based on a set of established rules. This can include making calculations, conducting searches, moving files, assigning tasks, or making connections. In short, RPA supports large-volume, repetitive tasks that don’t require additional decision-making or judgment.
There are many uses for software robots, but applications in data management have started to reach our industry. By applying automation towards data management, lenders and vendors can proactively gather information, organize data, send and receive alerts, and provide regular updates. Specifically, Allied Solutions uses RPA technology in our automated web verification process as a proactive measure in tandem with our ongoing Electronic Data Interchange (EDI) capabilities. RPA is utilized to automate insurance verification by proactively monitoring insurance statuses and recording active and inactive insurance on loans with available information. Utilizing RPA can help reduce the number of borrower notices sent, which in turn can lessen borrower noise and false placement.
Related Content: "Tips to Protect and Grow Your Loan Portfolio Beyond the Pandemic"
5 Data Management Opportunities for Financial Institutions
Data utilization and consumption are not slowing down or going away. AI capabilities and adoption are becoming more prevalent across industries and sectors. Particularly, as businesses reevaluate operations in our post-COVID environment, opportunities for AI are going to emerge. These opportunities extend to financial institutions as well, particularly when it comes to data management for loans and collateral.
- Adapting to the Magnitude of COVID-19
Since COVID-19 emerged toward the end of 2019 and exploded to become a global pandemic in March 2020, millions have been infected. For many of us, life and business changed abruptly. Now, looking forward in a post-pandemic world, leaders are learning to adapt to a ‘new normal’ as the virus continues to disrupt day-to-day operations. For financial institutions, deferrals, forbearance, extensions, modifications, and more have disrupted the industry with halted operations or adapted processes. Opportunities to enhance or automate aspects of the data management process could help institutions manage an accumulating backlog of collections, recoveries, and cancellations due to current economic relief practices. - Establishing a Comprehensive Risk Strategy
Proactive risk management is becoming critically important for lending institutions to be prepared and adapt to the changing needs of their consumers and protecting their loan portfolios. In today’s socio-economic environment with changing regulations and guidance, a comprehensive approach can help institutions make more accurate, informed decisions. Leveraging AI tools, such as RPA, can help enrich existing data and process updates and changes in close to real time so institutions can provide optimal service to their consumers and reallocate human resources where needed. - Handling an Immense Amount of Data
As more information goes digital, there are increased opportunities to gather and leverage data. Data is an important tool to identify strengths and weaknesses within a lender’s risk strategy and portfolio. AI tools can help institutions better understand their risk exposure, verify loan information, track changes in information, or even predict future consumer behaviors by watching different data variables. - Meeting Consumer Expectations
Consumer expectations are shifting. As digital tools emerge and more consumers leverage online resources for services and purchases, the expectation for personalization is growing. Consumers are used to targeted advertising, personalized messaging, and sharing their private data with third parties as an access requirement. Multiple studies report that a majority of consumers are responding to personalization as a way to cut through the noise and receive information that is relevant specifically for them. AI tools help gather, sift, and direct this data to help identify consumers’ needs and wants. For financial institutions, this could mean specific information related to current loans, future business opportunities, and tracking ongoing behaviors. - Utilizing Emerging Technologies
Technological resources continue to emerge to support growing industry needs. AI-driven technologies, in particular, have helped improve process efficiencies and the customer experience. This ‘intelligence’ software is being built to address challenges, improve efficiencies, help scale growing services, and sift through increasing data sources. By assessing current challenges and business needs, financial institutions have opportunities to utilize new intelligence software or partner with vendors to leverage and organize data or increase efficiencies by allowing robotics help run manual service processes.
Technology is always changing, and with the introduction and wide adoption of artificial intelligence, opportunities are emerging to help financial institutions serve their consumers, protect their portfolios, and prompt new growth opportunities.
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