Case Studies

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Case Studies

  • CLIENT’S KYC BUSINESS CHALLENGES
    • KYC (know your customer) related data was not at one data center
    • KYC data was not classified, semantic and ontology were not applied.
    • KYC data was not accurate, complete and consistent
    • KYC data was in multiple formats and data was duplicate
    • KYC data was scattered in different files, databases and formats
    • KYC data was not governed
    • KYC data acquisition, storage was not automated
    • KYC data analytics mechanism was not setup
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    Z&As services and Solutions:

    We worked with our client, understood the current state landscape of their KYC and customer onboarding solutions, which included tens of different onboarding / KYC platforms covering 30+ countries, and implemented a single virtual KYC strategy. As part of the virtual KYC strategy, the total number of KYC and onboarding platforms was reduced from tens to three, and an information fabric data broker was built to enable information to be shared across the three different KYC platforms (within global data privacy regulation rights) so that it operated as a single virtual KYC platform.


    Value addition and Impact to client:

    • Clients were able to acquire, transform, store, manage, remediate and analyze their KYC data quickly, efficiently
    • Saved millions of dollars fines from regulatory agencies
    • Reduced KYC process errors and time saving
    • Improved KYC process efficiency
    • Enabled trusted KYC data
  • CLIENT’S CLOUD RELATED CHALLENGES
    • Cloud Strategy- Moving to the cloud, where and how to start
    • Which cloud partner or service provider to chose
    • Which cloud service delivery model to select. Either Public or Private or Hybrid
    • Cloud services and deployment models (IaaS – Cloud infrastructure as a service, PaaS- Platform as a service, Saas- Software as a service, BPaaS- Business process as a service)
    • Which applications to consider for the cloud
    • Object storage
    • Data and information Security in cloud
    • Regulatory requirements & concerns
    • Compliance requirements & concerns
    • Cloud based solution delivery (over mobile devices or over the internet)
    • Cloud based operating models
    • AML/ Fraud detection, KYC, Customer analytics and Payments related cloud based applications
    • Analytics in Cloud (AI- Artificial Inelegance, ML- machine learning, DL- dep learning, NLP- Natural language processing)
    • Selections and training of cloud experts
    • Develop SLAs (Service Level Agreement)
    • POC (Proof of Concept), POV (Proof of Value), Piloting and operationalizing cloud based application deployments
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    Z&As services and Solutions:

    We worked with our clients, understood their challenges and provided our services that resulted in solving all above-mentioned cloud related challenges.


    Value addition and Impact to client

    • Clients were able increase turnaround time for cloud based AML, KYC, Payment related solutions and services
    • Clients were able to start small and grow big quickly with cloud/object storage
    • Clients were able to gain ROI due to cloud strategy and capabilities
    • Clients were able to go global and provide services to multiple customers in few minutes
    • Clients were able to detect and prevent potential fraud activity for AML use case
    • Migrated and deployed selected use cases in cloud with success using either Amazon AWS or Google GCP or Microsoft Azure
    • Clients were able to perform analytics in cloud and will be able to do In future when data is exploding every day
    • Develop cloud Center of Excellence and became data driven business
    • Increase market cap for mission critical business application
    • Clients overall cost is reduced as pay-per-use cloud delivery model is deployed
  • CLIENT’S ENTERPRISE DATA (OBJECT) STORAGE RELATED CHALLENGES
    • Block storage and file storage limitations and costs
    • Need new innovative, salable, cost effective, secure, preformat Object storage strategy, architecture, design, deployment models for applications in cloud
    • Object storage Cloud services and deployment models (IaaS – Cloud infrastructure as a service, PaaS- Platform as a service, Saas- Software as a service, BPaaS- Business process as a service)
    • Object metadata storage
    • Object storage of Structured and unstructured data
    • Data object shortage security in cloud
    • Multitenancy for on-demand object storage service
    • Regulatory requirements & concerns around object storage
    • Compliance requirements & concerns around data around object storage
    • Cloud based data (object storage) delivery (over mobile devices or over the internet) using RESTful / AWS S3 APIs
    • Developing object storage SLAs (Service Level Agreement), Policies, Procedures
    • POC (Proof of Concept), POV (Proof of Value), Piloting, operationalizing Object storage model
      •

    Z&As services and Solutions:

    We worked with our clients, understood their challenges and provided our services that resulted in solving all above-mentioned object storage related challenges.


    Value addition and Impact to client

    • Clients were able increase turnaround time for storing cloud based AML, KYC, Payment related object storage
    • Clients were able increase turnaround time for storing cloud based AML, KYC, Payment related object storage
    • Clients were able to start small and grow big quickly with cloud/object storage
    • Clients were able to gain ROI due to innovative object storage strategy and capabilities
    • Clients were able to go global and provide services to multiple customers in few minutes due to scalable object storage deployment model
    • Migrated and deployed selected use cases using Object storage model with success using either Dell EMC Isilon or Amazon S3 or Google GCP or Microsoft Azure or IBM cloud object storage models
    • Clients were able to perform analytics in cloud
    • Develop scalable object storage model and became data driven business
    • Increase market cap for mission critical data and analytics
    • Clients overall cost is reduced as pay-per-use cloud object storage delivery model is deployed
  • CLIENT’S BLOCKCHAIN AND BITCOIN RELATED CHALLENGES
    • Blockchain based applications SWOT analysis, strategy, design and deployment
    • Digital cash, digital business strategy and Blockchain consortium
    • Global Peer-to-peer Block chain based payment and lending platform
    • Asset tracking
    • Trade finance
    • Credit ratings
    • Block chain based transaction and payment mechanism is not backed as well as not regulated by any government to create new trust model
    • POC (Proof of Concept), POV (Proof of Value), Piloting, operationalizing Block chain/Bitcoin based crypto currency applications
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    Z&As services and Solutions:

    We worked with our clients, understood their challenges and provided our services that resulted in solving all above-mentioned Block chain and bitcoin related challenges.


    Value addition and Impact to client

    • Clients were able to see decrease intermediate parties, reduce fraud, reduce cost, reduce time in processing secure transactions
    • Client were able to access real time blockchain transaction
    • Block chain enabled cross border remittances and enhances spending powered of recipients
    • Clients were able to gain increased transparency in trade finance, credit rating, credit history, risk assessment procedure
    • Clients were able to see increased security and safety as no one could hack or change block lauder entry with ought consensus of other participating parties
  • CLIENT’S AML ( ANTI-MONEY LAUNDERING) BUSINESS CHALLENGES
    • AML data was poorly managed
    • Scenario thresholds were not throttled using an effective risk-based customer segmentation
    • AML Transaction Monitoring platforms generated too many false positives
    • Time to implement new scenarios was too long
    • Cost of meeting BSA Compliance was growing at 20%+ year-over-year
    • Unable to meet regulatory requirements around SAR filings within 90 days of the suspicious event
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    Z&As services and Solutions:

    We worked with our client to implement a three-step plan to remediate their AML challenge. The first was to improve the quality of the customer data being used as inputs into segmentation models and monitoring scenarios. This incorporated the use of new and innovative entity resolution techniques using the client’s Big Data cluster to master customer and account data that was missing critical data elements. The second step was to improve the data flow pipelines and simplify the data lineage from getting authoritative transaction source data, enriching it with customer master data and feeding it into segmentation and scenario models.

    An effective end-to-end ELT pipeline was built in the customer’s Big Data environment. The final step was to use the Big Data environment to facilitate the case management and investigations process by loading all prior cases, screening alerts, KYC data, Lexis-Nexis external negative news data and transaction history into an easy to use investigator’s research tool running on ElasticSearch’s Lucene index. This reduced the amount of different systems that investigator’s needed to access to do their jobs from 19 to 3.


    Value addition and Impact to client:

    • False positive reduction of 15% within the first 3 months of production go-live
    • Increase investigator productivity and workload from 2 cases per day to 8
    • Acknowledgement from independent auditors and external regulators that the program was a success
  • CLIENT’S RISK DATA RELATED BUSINESS CHALLENGES
    • Risk related data (return on risk weighted assets) was not at one data center
    • Risk related data was not classified, semantic and ontology were not applied
    • Risk related data was not accurate, complete and consistent
    • Risk related data was in multiple formats and data was duplicate
    • Risk related data was scattered in different files, databases and formats
    • Risk related data was not governed
    • Risk related data acquisition, storage was not automated
    • Risk related data analytics mechanism was not setup
    • Risk related was not properly calculated
    • Expensive Risk investigation process
    • Time consuming risk calculation & detection process
    • Risk related data models and engines were not accurate.
      •

    Z&As services and Solutions:

    We worked with our clients, understood their challenges and provided our services that resulted in solving all above-mentioned risk related data and model related challenges.


    Value addition and Impact to client

    • Clients were able to acquire, transform, store, manage, remediate and analyze their Risk related data quickly, efficiently
    • Saved millions of dollars fines from regulatory agencies
    • Reduced Risk related process errors and time saving
    • Improved Risk calculation process and improved efficiency
    • Trusted Risk data
    • Trusted Risk investigation process
    • Reduces Risk case investigation time
  • CLIENT’S DATA GOVERNANCE RELATED BUSINESS CHALLENGES
    • Products and instruments related data was not at one data center
    • Products and instruments data was not classified, semantic and ontology were not applied
    • Products and instruments data was not accurate, complete and consistent
    • Products and instruments data was in multiple formats and data was duplicate
    • Products and instruments data was scattered in different files, databases and formats
    • Products and instruments data was not governed
    • Products and instruments data acquisition, storage was not automated
    • Products and instruments data analytics mechanism was not setup
      •

    Z&As services and Solutions:

    We worked with our clients, understood their challenges and provided our services that resulted in solving all above-mentioned Products and instruments related challenges.


    Value addition and Impact to client

    • Clients were able to acquire, transform, store, manage, remediate and analyze their Products and instruments data quickly, efficiently.
    • Saved millions of dollars fines from regulatory agencies.
    • Reduced Products and instruments process errors and time saving
    • Improved Products and instruments process efficiently
    • Trusted Products and instruments data
  • CLIENT’S BIG DATA AND OPERATIONAL ANALYTICS RELATED CHALLENGES
    • Client’s Enterprise Data Lake was not meeting business-expected service levels
    • Could not meet uptime and performance SLAs
    • Data acquisition processes were too slow and ineffective. Major backlogged pipeline of sources to ingest.
    • Data inventory management was manual and too difficult to update. It was too difficult to find the right data in the Data Lake
    • Capacity was not being shared effectively in the lake; some business overconsumed and got charged less than others who rarely used their allocated capacity.

    Z&As services and Solutions:

    We worked with our clients to completely re-do their Big Data operational model and platform. This included implementing new technologies for cluster management and performance improvement, dynamic cluster resource allocation technologies that improved how YARN distributed memory, CPU and disk utilization, new data acquisition tools leveraging Change Data Capture techniques versus traditional SQOOP, and new data discovery technologies that automated the scanning and semantic tagging of data to enable business users to search and find data in the lake. We also trained their platform operations teams to be able to run the cluster more efficiently.


    Value addition and Impact to client:

    • Reduced the backlog of data sources to be ingested, and improved the quality of the ingestion process in the lake with superior controls to ensure that file integrity was intact throughout the data movement.
    • Big Data Lake had its first ever Semantic Data catalog, enabling business users to search and access meta-data about the various business applications in the lake.
    • BigIncreased platform uptime and runtime by over 90% within the first 4 months of go-live. Enabled the IT organization to meet business uptime and runtime SLAs for the first time.
    • BigImproved resource allocation and utilization by 67%. This enabled dynamic chargeback model to more effectively price and recover costs for the Big Data Lake based on actual consumption and not user forecasts.