The Value of Integrating Artificial Intelligence into CRM Systems
Artificial intelligence (AI) is one of the fastest emerging technologies within the financial sector. Although the financial sector is usually the slowest to adopt technologies due to its rules and regulations, the widespread adoption of SaaS solutions has brought AI into the industry. This blog post will go over the value of integrating artificial intelligence into customer relationship management (CRM) systems.
With the growth of unstructured data and the scale at which it is generated through social media posts, emails, audio transcripts, videos, and more, artificial intelligence and machine learning (ML) are causing financial services to reevaluate their infrastructures. In 2020, software accounted for more than 80% of all AI revenue, as more enterprises, especially financial firms saw the value in integrating their CRM systems with AI technology.
Large financial firms like JP Morgan Chase, Barclays, Citi, Goldman Sachs, and Capital One to name a few, are reaping the ROI benefits from AI's impact on automation, customer engagement and retention, and risk management. The world's leading financial firms realize the advantages that AI brings to the point that Goldman Sachs invested $72.5 million in AI and AutoML tools in 2019.
As SaaS platforms are now embedded into the majority of small-scale to already established firms' infrastructures in managing customer relationships, integrating AI will become a necessity to remain competitive in the financial market. For more predictions about how AI will transform the financial industry in 2021, check out the Top Five Artificial Intelligence Trends in Finance for 2021.
Here are four ways that integrating artificial intelligence with CRM solutions brings value across the front, middle, and back-office operations in financial firms.
1. Artificial Intelligence Product/Service Customization
Within CRM software lies enhanced contact management and the ability for financial teams to build and manage client relationships. Artificial intelligence uses insights gleaned from customer data to personalize a customer's experience with the firm by targeting their interests and goals. Based on client information input into the CRM system and client meeting notes and interactions, business analysts and product and portfolio managers can use intelligent tagging and smart search to keep up with customer interactions and transactions.
Fintech firms like Refinitiv, NCino IQ, and Accern, are enabling intelligent tagging and search capabilities through easy integrations with financial firms' CRM systems.
In the era of digitalization and data, consumers are now more drawn to companies that understand their customer journey and can personalize their services—especially when it comes to finance. Some of the largest financial institutions like Barclays, American Express, Discover, and others create custom offers on banking products and services based on real-time consumer interactions.
Whenever a client connects within a channel, the AI-decisioning engine reacts through the real-time action and considers the customer's recent activity and other behaviors. The AI model then predicts what the next best action or offer would be such as a customized ad, an outbound email or a notification to a teller, etc.
2. AI-Powered Chatbots
From the insights that CRM platforms are bringing to financial teams, financial services are taking client interactions one step further with AI-driven chatbots. AI-driven chatbots can trace customer information from CRM systems and act as virtual assistants to personalize interactions between a customer and its financial institution. AI-powered chatbots are also being utilized as on-demand personal financial advisors to customers.
For example, since introducing Erica, Bank of America's virtual assistant, the firm reported that it's chatbot using artificial intelligence pulled in an extra 3 million users within a 10-month time frame and experienced a 2x jump in engagement with clients on the mobile app within a month. Erica offered clients a virtual assistant and unique digital banking experience which encompassed predictive analytics, voice, and in-app messaging. Beyond providing customers with a personalized experience on a daily basis, Erica also helps the bank obtain insights on new and improved products and services.
3. Predictive Analytics
As new data is generated on customers daily, financial organizations have more information on customers than ever before. Thanks to technological advances, combining CRM and machine learning enables patterns in data to be captured on customer interactions and transactions. As AI and ML take note of changes in customer behaviors and then analyze the data, these tools can predict a client's future transactions and behaviors with a non-bias approach.
One of the ways that financial firms use predictive analytics is to mitigate risk. The most common predictive analytics use case is to predict creditworthiness. For example, financial institutions pull credit scores using FICO, which uses statistical analysis to predict consumers' behaviors. Looking at your past payment history, the analysis predicts how likely you are to miss a payment in the future. Additionally, consumers' behaviors and actions are compared and FICO calculates a score based on the performance of similar borrowers. Using this data, financial providers can identify a customer's historical transactions and interactions and predict a customer's future behaviors, reducing risk.
By integrating AI tools, financial enterprises can save time in deploying predictive analytics models. Companies like Teradata, Accern, and Cloudera, offer a platform where non-technical and technical end-users can import data and build and deploy predictive models quickly to score data and predict outcomes.
4. Using CRM AI for Automation
Integrating AI into CRM systems can relieve tedious and manual processes that financial services sales professionals spend doing, freeing up time to focus on more productive tasks that will lead to sales and higher client retention. According to Forbes, sales representatives spend less than 38 percent of their time actually selling. One of the reasons for this that salespeople explicitly called out was the lack of capabilities in their CRM system.
AI can relieve the frustration that salespeople feel by automating manual processes such as emails and data entry, capturing specific data, compiling customer reports, generating custom emails and personalizing communications with current and potential customers, and more. Sales representatives will also experience less human error with AI workflows.
For more information about how you can integrate AI into your CRM, request a demo below.
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About Accern:
Accern enhances AI workflows for financial service enterprises with a no-code data science platform. Researchers, business analysts, data science teams, and portfolio managers use Accern to build and deploy Natural Language Processing (NLP) models with artificial intelligence (AI). The results are that financial firms cut costs, generate better risk and investment insights, and experience a 24x productivity gain with our industry-leading NLP solutions. Allianz, IBM, and Jefferies utilize Accern to build and deploy AI solutions powered by our adaptive NLP and forecasting features. For more information on how we can accelerate AI adoption for your organization, visit accern.com.