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Inclusion Does Not Stop Workplace Bias, Deloitte Survey Shows

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In Deloitte’s 2019 State of Inclusion Survey, 86% of respondents said they felt comfortable being themselves all or most of the time at work, including 85% of women, 87% of Hispanic respondents, 86% of African American respondents, 87% of Asian respondents, 80% of respondents with a disability and 87% of LGBT respondents. But other questions in the company’s survey show a more troubling, less inclusive and productive office environment, and may indicate that simply implementing inclusion initiatives is not enough to prevent workplace bias.

While more than three-fourths of those surveyed also said that they believed their company “fostered an inclusive workplace,” many reported experiencing or witnessing bias (defined as “an unfair prejudice or judgment in favor or against a person or group based on preconceived notions”) in the workplace. In fact, 64% said that they “had experienced bias in their workplaces during the last year” and “also felt they had witnessed bias at work” in the same time frame. A sizable number of respondents—including 56% of LGBT respondents, 54% of respondents with disabilities and 53% of those with military status—also said they had experienced bias at least once a month.

Listening to those who say they have witnessed or experienced bias is especially important. When asked to more specifically categorize the bias they experienced and/or witnessed in the past year, 83% said that the bias in those incidents was indirect and subtle (also called “microaggression”), and therefore less easily identified and addressed. Also, the study found that those employees who belonged to certain communities were more likely to report witnessing bias against those communities than those outside them. For example, 48% of Hispanic respondents, 60% of Asian respondents, and 63% of African American respondents reported witnessing bias based on race or ethnicity, as opposed to only 34% of White, non-Hispanic respondents. Additionally, 40% of LGBT respondents reported witnessing bias based on sexuality, compared to only 23% of straight respondents.

While inclusion initiatives have not eliminated bias, Deloitte stresses that these programs are important and should remain. As Risk Management previously reported in the article “The Benefits of Diversity & Inclusion Initiatives,” not only can fostering diversity and inclusion be beneficial for workers of all backgrounds, it can also encourage employees to share ideas for innovations that can help the company, keep employees from leaving, and insulate the company from accusations of discrimination and reputational damage.

But building a more diverse workforce is only the first step, and does not guarantee that diverse voices are heard or that bias will not occur. Clearly, encouraging inclusion is not enough and more can be done to curtail workplace bias. And employees seeing or experiencing bias at work has serious ramifications for businesses. According to the survey, bias may impact productivity—68% of respondents experiencing or witnessing bias stated that bias negatively affected their productivity, and 70% say bias “has negatively impacted how engaged they feel at work.”

Deloitte says that modeling inclusion and anti-bias behavior in the workplace is essential, stressing the concept of “allyship,” which includes, “supporting others even if your personal identity is not impacted by a specific challenge or is not called upon in a specific situation.” This would include employees or managers listening to their colleagues when they express concerns about bias and addressing incidents of bias when they occur, even if that bias is not apparent to them or directly affecting them or their identity specifically.

According to the survey, 73% of respondents reported feeling comfortable talking about workplace bias, but “when faced with bias, nearly one in three said they ignored bias that they witnessed or experienced.” If businesses foster workplaces where people feel comfortable listening to and engaging honestly with colleagues of different backgrounds, create opportunities for diversity on teams and projects, and most importantly, address bias whenever it occurs, they can move towards a healthier, more productive work environment.

Tips to Prepare Your Organization For An Older Workforce

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People are living and working longer today than in the agricultural and industrial ages. The growth in the number and percentage of individuals over 60 and 80 years of age is already having a global impact.

From 1980 to 2017, the number of individuals over the age of 60 doubled to roughly 900 million. This segment of the world’s population will double again by 2050 to nearly 2 billion, according to the 2017 World Population Prospects report by the Department of Economic and Social Affairs of the United Nations Secretariat.

Risk professionals can prepare their organizations for the coming changes and opportunities of an older workforce with the following strategies:

  1. Customize a workplace safety program. Organizations can utilize various levels and different methods of training to improve safety awareness.
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    These include new hire training, annual mandatory compliance refreshers, on-the-job training, shadowing and formal mentoring programs, educational programs, and certifications. Training can focus on areas such as safety awareness, new technology, ergonomics and workstation setup, life skills, and other soft knowledge. This will also help with safety in general among the entire staff.

  1. Update the education and onboarding process. An important consideration is how different generations of employees learn, so specific training methods tailored to each generational group can be offered. Where online training modules may work for younger employees, older employees may prefer on-the-job or in-person training. It is up to each company to best identify the methods for training its workforce so the content of the training is effectively delivered and understood by its intended audience.
  2. Review training styles. In addition to receiving ongoing training, older employees may feel more engaged if they are asked to teach newer or less experienced employees. One area often overlooked is training for managers who may have older employees under their supervision. Much has been written about training and approaching millennials, however, the reverse is an emerging risk. Companies should begin focusing efforts on how to relate to and the best way to supervise older workers. This is an area of opportunity to enhance a company’s culture and develop the employee-employer relationship.
  1. Know a role’s physical demands. Employers need to ensure they have a good understanding of the actual physical demands of each job position in addition to the physical limitations of individual employees.
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    Post-offer and pre-employment functional capacity exams are recommended for all age groups in industrial and manufacturing sectors. Job rotation is an important safety tool, and can be used for all age groups in an effort to break up the monotonous nature of the work, avoid fatigue, and ultimately develop a well-rounded staff that can cover gaps as needed.

  1. Consider the intersection of technology, comfort and well-being. There are many low- and no-cost ideas that can make the workload more manageable for older employees. For example, in its Dingolfing, Germany plant, BMW hires older workers on an auto assembly line with accommodations for their age such as larger computer screens, special shoes, and chairs for some operations. And Microsoft offers an online Guide for Individuals with Age-Related Impairments, showing older workers how to create slower-moving pointers or magnified screen displays by adjusting their computer’s settings. Standard workstations can be improved with ergonomics in mind. Features like built-in back support in office chairs, standing desks, lighting created to minimize shadows and dark zones, and desks that are easily adjustable all contribute to employees’ comfort and minimize discomfort. On-site clinics save time and are geared toward prevention as well as early disease detection. Investing in the health of all employees through wellness programs is a timeless and ageless benefit and will contribute to productivity and reduce costs.
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  1. Promote an age-diverse business culture by recognizing and appreciating the skills/values of older workers. There are common misunderstanding and stereotypes with older employees that they are less efficient than their younger co-workers. However, from the Organisation for Economic Co-operation (OECD) in 2016 that the working proficiency (in both literacy and numeracy) of older employees is actually not significantly lower than their younger peers. In countries like the U.S., the proficiency of older workers is even at the same level as younger employees (see below charts). A follow-up study in 2018 by OECD indicated that older employees are more likely to involve in more complex tasks, such as supervise colleagues, have higher task discretion, use planning skills and influence others, which makes them as valuable assets as their younger co-workers. So it is important to promote an age-diverse business culture to appreciate the skills and value of older workers.
  1. Improve training against discrimination and negative attitudes to older workers on hiring, termination, compensation, and promotion. As risk management professionals, it is important to remind your organizations to review and improve the policy against discrimination and negative attitudes to older employees, in order to mitigate the potential legal risk. A 2013 AARP study indicated that “64% of U.S. workers have either experienced or observed age discrimination.” Given this background, in 2016, EEOC received 20,857 charges of age discrimination, which counted for more than 20% of all discrimination charges received by EEOC.

As the global working population continue to grow older, corporations around the world could expect to see more age discrimination litigations to come. Risk managers can play an important role by taking initiatives to help their organizations against discrimination and negative attitudes to older employees.

Several members of the RIMS International Council contributed to this article.

Using Adaptive Behavioral Analytics to Detect Fraud

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While fraud threats are nothing new for payments processors and financial institutions, the degree and magnitude of such incidents have escalated in recent years. A February 2018 Javelin study found that nearly 16.7 million consumers were victims of identity fraud in 2017—up 8% from the previous year.

Fraud prevention solutions must be flexible and sophisticated enough to not only counteract increasingly-savvy fraudsters, but also distinguish true fraud from false positives, which occur when genuine activity is mistakenly treated as fraud. According to CreditCards.com, four out of five blocked transactions are actually genuine, and these misunderstandings often result in customers being locked out of their accounts. In many ways, the aftermath of false positives can prove more damaging and costly than an actual instance of fraud, as institutions miss revenue generation opportunities while simultaneously hindering customer loyalty and trust.

As consumer payment technologies evolve, so too will the complexities of fraud detection and mitigation. Therefore, it is vital that risk management teams end their reliance on rigid, manually-programmed rule sets or static machine learning models and instead capitalize on the advanced capabilities offered by today’s more versatile tools. By modernizing their fraud strategies with adaptive behavioral analytics, payments processors and financial institutions can better mitigate risk and increase revenue.

How Does it Work?

Unlike the static machine learning of the past, adaptive behavioral analytics are extremely proficient at differentiating between actual fraud and activities that appear suspicious but are ultimately genuine. As a result, friction in financial services and e-commerce is significantly reduced and customers can maintain confidence in their preferred transaction method.

Adaptive behavioral analytics empowers machine learning through a set of sophisticated, automated, self-learning algorithms that review account activities and notify security teams of anomalies.

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These algorithms construct baseline behavioral profiles to reflect a customer’s activity type and frequency. In every interaction—regardless of if a payment occurs—information is gathered and evaluated on the type of device that is used, how it’s used, its location and the amount of the purchase. Combined, these behaviors create a customer portrait that becomes increasingly more accurate over time. Every subsequent interaction then can be measured against the behavioral portrait, within milliseconds, to determine if their activities are fraudulent or genuine.

For example, if a user logs into his or her account at an abnormal rate or suddenly begins adding priority shipping to high-priced orders, the system will detect the irregularity and block future activity. However, if a user simply purchases an expensive holiday gift or books travel arrangements—behaviors that coincide with seasonal activity—the system will recognize and differentiate the fraudulent from the legitimate accordingly.

Adaptive behavioral analytics also optimizes the speed and convenience of fraud detection by processing volumes of data and delivering critical intelligence accurately and immediately. Through this more comprehensive investigation, the software enhances the customer profile to better understand and recognize behavioral trends—a welcome sight for security teams that previously spent hours sifting through reports to locate red flags.

Where Can Adaptive Behavioral Analytics Help Most?

The ubiquity of mobile technology has created a consumer audience who prefers to conduct business through a smartphone, tablet or another device that eliminates a trip to a physical store or bank branch. In turn, these consumers demand leading-edge mobile technologies that are intuitive, convenient and offer a full range of services.

The combination of the U.S. adoption of the EMV standard in 2015 and the rise in e-commerce has escalated the volume and prominence of Card Not Present (CNP) fraud. Whether through online purchase portals or apps that access mobile wallets, the digital entry of account information raises the likelihood of a person’s information becoming compromised.

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With more transactions taking place, the volume of both true fraud activity and regular behaviors that appear suspicious will increase. However, adaptive behavioral analytics enables a more refined detection between the actual fraud and genuine activity.

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It is the best of both worlds: a much-needed, innovative line of defense that combats payments fraud and clears a path for more revenue-generating transactions.

Mitigating Risk with Predictive Modeling

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One of most effective risk management philosophies is to work smarter, not harder, implementing holistic tools, such as predictive analytics to ensure it is minimized. More often than not, companies implement blanketed management programs, applying the same strategies to all employees regardless of performance. With this approach, employers waste time and effort focusing on employees who are not at risk, leaving room for at-risk employees to go unnoticed. On an opposing front, many companies use the “squeaky wheel” approach, diverting all of their attention to employees that actively demonstrate troublesome behaviors. While this approach targets a greater amount of at-risk employees, it still leaves room for some to go undetected.

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Alternatively, a strategic employee-specific management program allows employers to identify at-risk employees regardless of how “squeaky” they are. The theory behind an employee-specific management program is simple – monitor your employees for changes that indicative risky behavior.

More often than not, these changes are subtle and undetectable to employers. Even with a team of risk management professionals, the necessary attention to detail is near impossible for companies with thousands of employees. So, how can we efficiently monitor for and detect these subtle changes?

Enter predictive modeling

Predictive modeling is an effective tool that addresses the needs of many industries – turning hundreds of thousands of data points into tangible data that can predict anything from consumer demands to credit scoring and anything in between. Challenging traditional personnel management practices, predictive modeling shines a light on the psychology behind today’s work force.

Predictive modeling has become an essential tool for companies across the globe, playing a role in nearly every industry, from marketing to finance, trucking, and the risk management sector. It provides employers with a unique look into the subtle, yet profound, fluctuations in employees’ behaviors that often go undetected. Examining thousands of data points and trends from past events, predictive modeling possesses the power to identify changes in behavioral patterns and predict the outcomes of future events, arming managers with the knowledge needed to proactively intervene with the right employee, on the right subject, at the right time to avoid events such as workers’ compensation claims and voluntary employee turnover.

With this information on hand, employers are able to replace their blanketed risk management program with a streamlined, employee-specific program, saving time and money—and most importantly, lowering risk. To understand the value offered through predictive modeling, one must understand that most employees would not be classified as “at-risk” at the time of employment. It’s the events that occur after the onboarding that mold the employee’s work behavior and create liabilities.

Notably, it is not just work-related problems that can put employees in the “at-risk” category. Often, medical or personal issues can cause changes in an employee’s work habits and behaviors. Tapping into historical data, predictive modeling is able to detect subtle changes and bring at-risk employees forward for remediation. With this information on-hand, managers can proactively connect with their employees to address an issue before it snowballs into a costly incident.

As one of the most risk-prone industries, the transportation space leverages predictive modeling to monitor employees for unsafe driving behaviors which can result in hefty violation fines and accidents. For example, if a driver is dealing with an ill grandmother, he or she may be paying less attention to the road and spending more time on the phone scheduling doctor appointments and responding to calls. Based on past performance, his or her manager will be alerted that the employee is hard-braking more than usual and spending more time in idle. By opening the channels of communication between the driver and manager, they can work together to identify a solution, whether it be an adjusted work schedule or a reduced workload.

Additionally, predictive modeling can help managers focus on causation rather than correlation. When an incident occurs, many managers tend to put emphasis on what happened, not why it happened. As a result, they often work to fix the correlating issue rather than addressing the root cause.

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By analyzing the data gathered through predictive modeling, managers can reflect on the changes in employee behaviors, corporate management or workload leading up to the incident. Recognizing the fluctuation leading up to the accident, managers can proactively monitor for similar incidents and intervene.

An example of this is a risk all managers dread – workers compensation claims. Many companies have accepted workers comp claims as a cost of doing business, failing to understand the factors leading up to the claim. Prior to filing a claim, an employee may be feeling under-motivated and overworked, often putting in the bare minimum and cutting corners with little attention to detail. The reduced attention span lands him or her in trouble when there is a resulting injury on the job and puts the company at risk for a costly claim. With predictive modeling, the manager is able to identify the changes in the employee’s work performance and identify the root cause. Further down the line, the manager can also monitor for similar situations and proactively work with the employee to make his or her work experience more positive.

As managers continue to look beyond traditional methods to better manage their employees and overall company operations, they will be able to capitalize on innovative technologies, such as predictive analytics, to help retain top talent, reduce risk, and build better, longer-lasting relationships with their employees. With growing adoption of proactive risk management solutions, today’s workplace will continue to become a safer, stress-free environment for all.