# Use Cases ### Custodial Sesame Data enables companies to deliver modern digital experiences and automate operations while keeping existing trust systems as the core. The [transactions](https://docs.landytech.com/sesame-data-v2/api-endpoints/transactions "mention") and [positions](https://docs.landytech.com/sesame-data-v2/api-endpoints/positions "mention") endpoints deliver rich transaction and position data per entity requested, and for the date range requested. Client systems will use this data to achieve the following outcomes: #### 1. Automate bookkeeping * Scale trust administration without linear headcount growth by utilising off the shelf integrations with 3rd party accounting and trust softwares, or integrating to another software * Enable T+1 bookkeeping, avoiding the post quarter-end rush * Reduce errors, write-offs, and compliance risk by ingesting all transactions more quickly and timeously #### 2. Improve AML risk-based monitoring * Rest assured knowing that Sesame will share up-to-date client transaction and position data daily, making this data available on demand for AML monitoring

A representation of the flow of data from custodians, through the Sesame file processing and data standardisation layer, and then made available via API in a common format, for consumption by software like Quantios QCore and PlainSail.

### Analytics Sesame Data feeds your existing systems with data, reporting, and insights you cannot source or produce yourself without adding new teams or replacing your core infrastructure. Analytics users can access a breadth of data, covering performance, PnL, risk, exposures and other position and transaction data. The [analytics](https://docs.landytech.com/sesame-data-v2/api-endpoints/analytics "mention") API allows users the flexibility to request only the data fields they want, joining data across entities, positions, transactions and assets. Client systems will use this data to achieve the following outcomes: #### 1. Data & Analytics Foundation * Normalise data across custodians and portfolios of private or alternative assets, under a single data model * Feed normalised data into existing systems via API or connectors * Eliminate manual data collection and having to build rules to unify inconsistent datasets #### 2. Operational Efficiency at Scale * On-demand reporting: Generate consistent reports across all asset classes without spreadsheets * Generate analytics more quickly, across hundreds of entities and with lower costs to produce #### 3. Client & Advisor Value * Equip relationship managers with timely, actionable intelligence to inform and guide their clients * Improve retention, wallet share, and client satisfaction