Enabling a Global Bank to simplify its Reconciliation and Reporting​

Leading Global Bank’s Shared Services Center


  • Cost team spending considerable time on downloading, processing and producing  data heavy month close files manually
  • Current tools provide limited insights and lack functionality to provide transaction level drill down 
  • Numerous ad-hoc requests being received and catered to during month end
  • Delivery of critical cost MI is dependent on manually intensive and iterative front to back reconciliation process which takes 15+ days
  • Nanobi’s LiquiData Platform automated and simplified the acquisition of structured and unstructured data across multiple applications
  • Insights generated using statistical techniques and sent to users who can then drill down & provide commentary
  • A two step Reconciliation process using Deterministic and Probabilistic which is layered on ML algorithms like Text, Pattern Detection, Phonetics and Edit Distance techniques to improve the matched records

The 3 Use Cases with different user groups within the bank working on

  • Insights on Cost Reporting

  • Cost Allocation and Reconciliation of Costs

  • Reconciliation of Trade, Nostro, Accounting


Overall Efficiency at a Global Level and reduction of costs in Year 1


Reduction in TAT time for month end reconciliation process to complete and No. of FTEs​


Increase in Auto reconciliation ​of trades

  • Expected improvement in efficiency to be about 10% globally
  • 40% increase in Auto-reconciliation of trade using ML/AI algorithm
  • Reducing the turn around time of reconciliation process from 15+ days to 6 days and reduction in number of FTEs by 30%
  • Self serve functionality expected to eliminate manual effort on month close files
  • Provide business and finance with the ability to create flexible dashboards and analytics