Secure Computing Platform

AI-based Predictive analytics is the best for combating fraud because it uses historical data, machine learning algorithms, and statistical modelling to predict future events and assists in automatically detecting instances and identifying fraud patterns for revealing potentially fraudulent claims. With a scientific approach to behavioural analytics, technology is assisting in the detection and prevention of fraud instances. AI-based predictive analytics is the most dependable model for detecting fraud and producing consistent results.


AtomDops Secure Platform is the E2E solution to combat the fraud.

  • -> Deep Mapping Analytics
  • -> Complex Modelling
  • -> Complex contextual analysis
  • -> Complex Logic
  • -> Real Time Monitoring
  • -> Manage Large Scale Event and Transaction Handling
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Our secure platform is designed with the below key parameters covering multi functions & operations to ensure E2E protection from cyber fraud & crimes, illegal intrusion, hacking, data breach & theft, etc.,

Integration Functions

  • • Communication services
  • • Message/Data formatting
  • • procession and transformations

UIX / Management Hub

Proactive dashboard ,  
Notifications ,
Alerts & Events ,   
Attack Predictions ,
IR / Reporting ,   
Case Management.

Analytic Functions

• AI ML enabled analytic model with well trained data models ensure past data analysed and predictive data visualized effectively.

Data Services Functions

• Historical Data, DLP, Security Logs, DBMS
• Alert / Sensor Systems, Network Analysers, Vulnerability Scanners & Honeypots / Probes

Security Operation Functions

• Orchestration, Automation & Response should be deep rooted and max.

Our Vision

Prevent bad actors from abusing the systems using best of technology.
Protect the sanctity of trust between orgs and consumers.
Protect the brand image of organizations by weeding out fraudulent transactions.

Trust drives the economy
Our platform ensures it is well guarded against sophisticated bad actors and ensures a healthy ecosystem that's sanitized.

Key Factors for any Bussiness

Trust the key for any
Bad Experience dents
Frauds across sectors
Conventional Systems

3 Critical Problem Statements

Data modeling and Data relationship mapping can be complex and recursive in nature because they involve analyzing and representing complex data structures and relationships between data entities. Despite the complexity of data modeling and data relationship mapping, they are important aspects of database design and development. Accurate and effective data modeling can help to ensure data integrity, improve data quality, and make it easier to manage and analyze large amounts of data.
If decision making on preventing frauds is reactive, it means that actions are taken after a fraud has occurred. Reactive decision making on preventing frauds can be a significant problem, leading to increased losses and damages. To address this problem, organizations can implement proactive measures, such as real-time monitoring and analysis, automated fraud detection systems, and predictive analytics. These measures can help to prevent frauds before they occur and minimize the impact of any fraudulent activity.
Batch processing systems are not real-time in nature because they process data in batches or groups, rather than immediately as the data arrives. There is a delay between when the data is generated and when it is processed, making the system non-real-time. In contrast, real-time systems process data immediately as it is generated, providing instant feedback and results. These systems are designed to handle high volumes of data in real-time, with very low latency or delay.

Our Thought Process

Our graph data lake can handle extremely complex data relations which is queryable.
Ingested data gets processed in real time by data processing clusters. Data is served fresh!
Our ML models run on fresh data thereby helping orgs take data driven decisions making that's real time.

We adopt latest technologies on Graph data lake and continuously optimizwd ML models can work and process extremely complex data mapping and relations.

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