top of page

Suspicious behaviour detection!

Monitor visible, hidden and cognitive behavioural patterns through keystrokes, time spent, ip, and device  parameters.

bg-service50.png

How it Works?

Using Machine learning (ML) to protect and secure, digital transactions at scale.

Device Fingerprinting

Identify devices and browsers used by genuine users which can tackle suspicious behaviour and account takeover. We protect Android & Apple apps through SDKs while covering Desktops & Laptops through javascript libraries. The approach offers a lightweight form of fraud prevention against fraudsters who use identity concealing techniques like disabling cookies, surfing through a VPN, or using browsers in incognito mode. 

Rectangle 503.png
device-finger-1.png
Group 2796.png
app.gif

Application interaction

Tracking application interaction involves monitoring a user’s journey in the application, the screens/page visits, frequency and duration of usage and thus the overall behaviour of a user.

Keystroke dynamics & Device Behaviour

Keystroke dynamics is a method of gathering and analysing speed and variation in typing behaviour to identify users. In mobile devices it also analyzes the angle at which the phone is held, finger pressure on the touchscreen, latency between keystrokes, duration of keystrokes, hold time and overall typing speed and frequency of errors.

Rectangle 503.png
keystroke-2.png
keystroke-1.png

Problem Statement

Group 2725.png
Strong Customer Authentication

Consolidate Insights From Various Data Sources For Real-Time Transaction Risk Analysis And Customer Profiling From Onboarding To Payments. 

Group 2726.png
Account Takeover

Uncover Account Takeovers And Fraudulent Activities In Real-Time Using Alternate Data And ML Models.

Group 2727.png
Social Engineering Scams

Analyse User Patterns And Data To Identify Even Subtle Deviations From The User Behaviour And Get Alerts In Real-Time.

Rectangle 503.png
secure-2.png
secure-1.png

Secure Innovative Products

Low value payments (UPI, Zelle, Faster Payments), BNPL, App based lending, P2P, P2M, P2A, RFP and a range of new products require models and rules customized to contain fraudsters & lower delinquency.

IP Fingerprinting

Data elements derived from IP addresses are used to understand Geolocation mapping, ISP, VPN Settings as well as identify possible access from TOR/Dark web, IPs with high Threat level or IPs used in DDOS. These serve as markers to detect any anomalies in behaviour and thus detect fraud

fingerprint_scanner.gif
Rectangle 503.png
faster-2.png
faster-1.png

Faster than a Bullet

Customer Expectations Of Response Time Have Dropped From Days To Minutes To Seconds To Milliseconds, Hence Fraud Needs To Be Detected Faster Than Ever Before.

bottom of page