By Sudhakar Raja, Founder & CEO, TRST Score
In the Banking, Financial Services, and Insurance (BFSI) sectors, the integration of Human Resources (HR) technology platforms with Artificial Intelligence (AI) has emerged as a key strategy to combat employee attrition and improve overall employee retention rates. Due to the dynamic nature of customer-facing positions, retaining a sizable workforce is a challenge for the entire industry, and even well-established corporations can experience attrition rates of more than 45%. Therefore, leveraging AI technologies has become crucial for identifying, addressing, and preventing primary factors that contribute to attrition. Among the noteworthy initiatives are:
Predictive Analytics— The BFSI sector, with its rich history of data-driven decision-making through credit bureaus, is adept at using data for insights. This expertise extends to building AI models that analyze historical employee data to recognize attrition-related patterns and trends. These models can predict employees at higher risk of leaving, enabling HR teams to intervene with targeted retention strategies. This proficiency in data analysis is beneficial to anticipate and mitigate staff resignations more effectively.
Sentiment Analysis— In the same manner the industry applies Know Your Customer (KYC) standards, AI-powered sentiment analysis tools scrutinize employee feedback from diverse sources like surveys, emails, and chats to gauge their satisfaction levels. Like their familiarity with video-based surveillance for compliance, the use of video analytics can provide greater insight into employee sentiments. This knowledge assists HR in identifying root causes of employee turnover and proactively addressing conflicts.
Employee Engagement Monitoring— Monitoring employee engagement levels through AI-driven analysis of interactions on communication platforms and collaboration tools, akin to AI-driven chatbots for consumer interaction, enables HR to detect disengagement in its inception. By recognizing disengaged employees and addressing their grievances, HR can mitigate the risk of attrition prior to their departure.
Personalized Recommendations— AI-powered platforms can provide suited suggestions for professional development and growth opportunities based on individual employees’ skills, interests, and career aspirations. This customization increases job satisfaction and discourages disengagement.
Performance Analytics— This involves assessing the employee’s performance based on various data elements, such as project completion, targets achieved, and feedback from managers and colleagues. This information guides HR to identify high-performing employees and reward their contributions appropriately.
Retention Strategies— AI can suggest targeted retention strategies for individual employees based on their profiles and the identified risk factors. Chatbots, surveys and performance analytics are some of the typical tools that are used to arrive at the data required for such analysis. These strategies might include mentorship programs, skill development initiatives, promotions, or changes in job roles.
Real-time Feedback & Workload Analysis— AI-powered virtual assistants allow employees to provide real-time feedback, allowing prompt problem resolution and showcasing the company’s commitment to employee well-being. With AI, employee workloads can be assessed, and instances of overwork or burnout can be flagged. This enables HR to allocate resources more efficiently and maintain a healthy work-life balance.
Diversity and Inclusion Initiatives— AI aids in identifying potential biases in HR practices that might contribute to attrition among specific groups, promoting inclusivity.
Exit Prediction— AI models forecast the likelihood of an employee leaving the organization within a specified timeframe. This early warning system empowers HR to resolve concerns and implement retention strategies before it’s too late.
Talent Mapping & Skill-Job Fit— Utilizing AI, HR can identify internal candidates suitable for open positions, reducing the need to hire externally. This is especially difficult in very large organizations. AI can rapidly examine through skill repositories and recommend internal candidates as replacements. This can also increase employee morale and engagement by providing growth opportunities. Moreover, AI assesses the alignment of an employee’s talents with their current position. When discrepancies arise, HR can consider role realignment or upskilling to enhance job satisfaction.
However, amid AI-driven advancements, organizations frequently disregard attrition-influencing human risk factors such as offer discrepancies, absences, and fraud. Despite the diversity of AI techniques, vulnerabilities continue to exist. These hazards significantly contribute to problems with attrition. To offset this, businesses must adopt a unified platform that utilizes employee data similarly to how credit data is utilized by organizations such as CIBIL. Risk silos can be eliminated by consolidating data across organizations. Today, compensation surveys, for instance, forecast industry trends and establish salary benchmarks. We could make a more informed and useful comparison if we had actual salary data. Background verification of employees is only a small part of the practice. Organizations must initiate risk mitigation across the entire ecosystem. Building this informational foundation is essential for gaining deeper insights. At the industry level, attrition prediction and personnel risk statistics make more sense. This paradigm shift could conserve resources and propel the sector forward.
In a broader sense, BFSI organizations capitalize on technology to improve employee retention through data and proactivity. Nevertheless, these technologies must be implemented responsibly, transparently, and with employee privacy in mind to generate greater confidence.
