A digital-first world has led to a proliferation in consumer touch points, which means businesses now have a much bigger attack-prone turf to protect. In the first quarter of 2022, almost every industry witnessed a spike in attacks across their digital front-ends.
Not only has the attack surface increased, fraud attacks are becoming increasingly complex, thanks to the intelligence that bots are gaining and easy access to cheap human click farms. Bad actors are also becoming adept at evading detection by devising ways to bypass fraud defense and anti-bot mechanisms. They resort to randomizing attacks to deliberately display inconclusive signals and obstruct visibility into the attacks.
All these factors combined together are making it increasingly difficult for businesses to tell bad actors from genuine consumers.
Most fraud defense solutions fail to capture subtle indicators of fraud
The problem is most fraud defense solutions either fail to capture the subtle signs of fraud or the signals captured are limited, opaque, and rarely updated. Often, these long-standing solutions follow a black-box approach to generate risk scores, providing businesses with no insights into the classification reasoning or signatures that contributed to the anomalies.
In the absence of logical explanations, businesses are less confident of the data received and are forced to spend significant amounts of time manually minimizing false positives and false negatives. This lack of actionable risk intelligence also impacts the businesses’ ability to adapt to the evolving attacks, despite making significant investments in fraud defense solutions.
Businesses, however, can no longer engage in a continuous cat-and-mouse game with attackers. They can neither keep absorbing fraud losses as cost of doing business or overburdening their internal fraud teams. They need to counter the attackers’ moves with data-backed decisions so they can ensure consumers’ account security long-term.
Black-box approach truncates risk intelligence
A lot of businesses, especially large enterprises possess volumes of data but are not able to harness useful data for actionable risk intelligence. They are often unclear about the data their fraud solution collects for risk assessment or how it calculates the risk scores. They don’t even have any data or explanations for the risk score calculated.
This black-box approach severely truncates the intelligence needed to make confident and accurate decisions. Since there is no significant actionable risk intelligence, businesses cannot share it across departments to improve decisioning across the user journey.
Ask questions, seek satisfactory answers
Risk intelligence plays an important role in fraud prevention as it can provide businesses with the required visibility into the evolving attack patterns. It is, therefore, imperative that businesses access the required intelligence to help detect the nuanced risk signals inherent in today’s sophisticated attacks.
When vetting a fraud prevention solution that's not only effective at catching attackers and minimizing disruption to good users, businesses need to pose some critical questions to their potential fraud prevention vendors to make risk intelligence actionable. These include:
- What data does the solution collect to identify risk?
- How is the risk score calculated?
- Does the solution provide the reasons behind the risk score so generated?
- Will the explanation provided be enough actionable information to help make confident, accurate decisions?
- Can this data be used and shared downstream with other departments to make better decisions across the user journey?
Enrich machine learning models with actionable risk intelligence
Arkose Labs provides businesses with actionable risk intelligence to fight evolving attack types with confidence. Our solution – Arkose Bot Manager – monitors device, IP, and behavioral data and aggregates it with the intelligence from across the Arkose Labs Global Network to detect any signs of impending attacks or monetization attempts.
The Arkose Bot Manager platform comes with more than 70 raw risk signals and 150 insights to empower businesses to fine-tune and enrich their machine learning models. Its granular dashboard enables businesses to dive deep into session data to clearly understand the rationale why the session was classified.
With data transparency at its core, Arkose Labs provides businesses with greater risk intelligence to help fight fraud efficiently and protect their consumers long-term. For instance, Dropbox leveraged Arkose Labs’ superior actionable risk intelligence to reduce intervention rate for customer experience by 70%.
To learn how you can benefit from Arkose Labs’ actionable risk intelligence, book a demo now.