Biometric: ‘Bio’ - human ‘metric’ - a unit of measurement. As the name suggests, biometrics refer to using unique biological and behavioral characteristics to help identify an individual.
Biometrics can refer to an entire spectrum of traits; let’s try and divide them into:
Strong (& obvious): Where verification happens because of the human body itself. These will be unique to each individual, fingerprint, iris recognition, and DNA (something that doesn't change even after death)
Then we have the ‘soft’ or ‘weaker’ measures: These are non-intrusive features of the human body analyzed in terms of how they manifest in behavior—the way we type, move, interact with a digital device, deliberate, hesitate, etc.
Even though most governments prefer strong biometrics for identification, multiple barriers prevent its implementation in the broader society. For example, shopping doesn’t necessarily require fingerprints unless you are out and about looking for Uranium.
There has been a dramatic increase in the use of softer biometrics for verification and authentication purposes, especially where financial transactions are involved. In fact - The global behavioral biometrics market size is projected to rise to an impressive 9.92 billion by the year 2030.
Passwords and patterns, even though helpful, are no longer enough. Passwords can be easily hacked, and patterns can be easily guessed.
What is causing the uptick in the use of soft biometrics?
1. Privacy laws
- Every individual state wants control over its data for both business and security purposes. However there are concerns of function creep, where the data is used for other purposes than what it was originally intended for.
- Users are a lot more privacy-conscious nowadays, and rightfully so. With the increasing prevalence of biometric systems, biometric data could be matched across various systems or databases without the individual’s awareness or approval. This could result in detailed profiling and monitoring, which would violate personal privacy.
In a nutshell: Strong biometrics like iris and fingerprint data are unique, permanent identifiers, making them sensitive and difficult to change if compromised, while soft biometrics like typing speed and mouse movement are behavioral patterns that are less invasive and don’t directly expose personal identity. See how Bureau does it.
2. Advancements in AI computation
- The cost of AI computation has decreased, making it more affordable to achieve sophisticated analysis of behavioral data that once required substantial resources, thus democratizing the use of soft biometrics.
- Advances in generative AI have enhanced the ability to correlate complex behavioral patterns, resulting in improved accuracy and more refined outcomes in the identification and authentication processes.
- The capacity to manage and analyze vast amounts of data has significantly increased, enabling real-time processing and interpretation of soft biometrics across large and diverse user bases, further enhancing their applicability.
3. Digital savviness
- User behavior has evolved through multiple digital revolutions, leaving behind rich trails that reveal user intentions and preferences.
- Consumers now have exceptionally high expectations for app experiences, assuming that their behavioral actions seamlessly guide and personalize their interactions.
How does one use Behavioural Biometrics analytics effectively?
An often quoted starter pack book for product managers is Steve Krug’s “Don’t make me think”. One of the key takeaways from the book is very simple - Intuitive software applications gain adoptions themselves.
The key word for us is intuitive here.
As technology improves, it allows us to give the user an experience where the next step is “intuitive”. The more intuitive this experience, the more they will adopt your services. But how do we measure and implement intuition? Thats where biometrics comes in.
What does the human body naturally tend to do? What do you as a user usually naturally tend to do? Behavioural analytics can manifest in many ways. One can also interpret this as understanding a person’s “humanness”.
Using behavioural analytics, businesses can optimize several areas across the user journey in any digital application, from launch to login to buy, and so on. Here are some examples:
- Understanding buyer preferences by analysing the parts of the website a user spent more time on
- By analyzing where users drop off during the onboarding process, businesses can identify confusing steps or points of friction. They can then streamline the onboarding flow, provide better guidance, or introduce tooltips to help users complete the process more smoothly.
- Studying how players interact with others in multiplayer or social aspects of a game (e.g., forming teams, participating in chat, joining guilds), businesses can enhance these features to make them more engaging, fostering a stronger sense of community and increasing player retention.
Related read: How To Tackle Gaming Fraud?
Behavioral Biometric analysis that shows signs of fraud
Let's take a look at some of the common depictions of “non-humannes” that would also signal that there are more sinister actors at play:
1. Fair Play in Gaming
Hackers often use tools like auto clickers to gain an unfair advantage in games by executing actions at speeds or with precision that exceed human capabilities.
These unnatural clicking patterns are a red flag, signaling potential foul play and compromising the integrity of the gaming experience.
2. Account Takeover
During an account takeover, a fraudster may exhibit behavior that deviates from the user’s typical interactions. This includes logging in from unfamiliar locations, using inconsistent typing rhythms, or accessing areas of the account in a sequence that differs from the usual user’s habits. These anomalies can trigger alerts for further investigation.
3. Remote Attack Prevention
Bots or malicious programs attempting to gain unauthorized access often display highly unnatural navigation patterns. For instance, a bot might move a cursor or navigate a webpage in a way that a human hand could never replicate.
These impossible paths are clear indicators of automated or remote attacks, prompting the need for additional security measures.
4. Fake Signups
Scammers with experience can breeze through registration forms with unnatural speed and precision, filling out fields without hesitation. This kind of flawless navigation through the signup process suggests that the user might be a bot or a human fraudster using automated tools, rather than a genuine customer.
5. Cart Conversion
Fraudsters often engage with e-commerce platforms in ways that differ from genuine customers. They may rapidly add items to a cart without the usual consideration or hesitation seen in legitimate shoppers.
Additionally, unusual pauses or overly efficient checkouts might indicate the use of scripts or automated tools designed to exploit the platform. Detecting these patterns can help in identifying fraudulent transactions before they’re completed.
Authenticating the human behind the screen
Today we want to focus on the underpinning element behind all of these use-cases that is understanding an individual through silent, implicit and intuitive behavioural trails that reflect the user’s core intentions like memory, hesitation, fluency, distraction and familiarity.
Essentially, we want to underline the fact that for certain repetitive, predictable and routine tasks, behavioural traits effectively lead to a unique profile for an individual. The most important area that comes in handy is authentication. In the case of authentication, through the manner in which a user signs in, say with an account and password, we will be able to know if it is the same person signing in.
Related read: Behavioural Biometrics for Frictionless Authentication
To simplify it further, there is so much muscle memory, speed and comfort in someone typing their childhood email address. ( A quick aside - I am sure that for most childhood email addresses, the typing pattern is probably a really strong biometric)
If we were to get technical about this, some of the elements that can make authentication more unique and profile-able to an individual would be:
- What is the dwell time on a screen page?
- What are the press and release time for specific keystrokes like?
- How deep is the tap as captured by a sensor like say the gyrometer?
- Are you a one-handed person or a dual-handed person?
- How quickly does one do this? What are the completion speeds like?
- What is the average scroll length?
Here are some visual representations of how behavioural biometrics is computed:
How does Bureau leverage behavioral biometrics to protect digital customers?
When you integrate with Bureau SDKs, there is a specific series of steps that follow:
1. Gathering diverse behavioral data from various customer touchpoints, including typing, tapping, holding, moving, and other actions.
2. Categorizing and classifying the collected data points into broader groups based on user behavior and overall population trends.
3. Detecting and identifying specific signals and triggers that emerge from patterns in the collected data, both past and present.
These signals can now be used to answer questions like these:
- Is this the same individual who is using the application?
- Does this individual appear to be using the application like the average person?
- Does this individual know the application too well? How fluent are they in navigation and knowing what to do in the application?
- Ofcourse, and the primary concern- is this even a real person?!
Bureau’s Behavioural Biometrics solution provides answers to all of these solutions.
Schedule a free demo with our team now!
