HOSTING & INTEGRATION
A detailed EM&AI Technical Architecture whitepaper is available upon request. Contact email@example.com or your EM&AI sales person for more information.
The EM&AI SDK allows banks and third-party vendors to develop their own virtual assistant user interfaces. It provides the system developer with:
- Secured access between the end user device and the EM&AI backend
- Secured access between the EM&AI backend and the bank’s data center
- Full range of UI widgets and user interactions
- Consistent handling of authenticated and unauthenticated interactions
- A sample application to help the developer get started
- Technical support
EM&AI SDKs are available for the following applications:
- Native iOS
- Native Android
- Direct REST APIs banks can expose on the internet, with authentication and/or IP whitelisting
- Proprietary agents, including cloud-to-ground agents to deploy into the EM&AI infrastructure
- The bank’s own SDKs
EM&AI can be easily deployed on the bank’s existing digital channels, including:
- Mobile enterprise apps (Android and iOS)
The technology can also be deployed on most social media platforms including WhatsApp, Facebook Messenger, and others.
For banks that use support agent systems (LivePerson LiveEngage, BoldChat, Sprinklr, etc.), EM&AI can be used in concert with these systems to answer end user questions more cost effectively, including seamless transfers between the virtual assistant and a human agent, when needed.
DEPLOYMENT & SUPPORT
- Initial set-up of a virtual assistant with out-of-the-box features, languages, and a trained AI model requires little to no effort from the bank.
- A EM&AI implementation fits the typical UAT production cycle and can be ready for review within a week. More time may be required depending on additional features and customizations requested by the bank.
- Once implemented, the bank will collaborate with EM&AI to design and customize content, train the data, work on APIs, and add unique information about products and services offered by the bank. A standard start-to-first-phase production cycle can be as short as three months.
- Once the production system goes live, the model continues to evolve and improve through ongoing usage data and subscription upgrades that will optimize performance.
Yes, supported languages include: English, French, Spanish, German. Additional languages can be easily added.
EM&AI can also accommodate subtle differences that occur regionally and from customer to customer. Native speakers and local testers are used to ensure the customer experience reflects the vernacular and unique requirements of each region.
No. The EM&AI enterprise-specific model already understands hundreds of enterprise terms and questions. This is because EM&AI aggregates enterprise utterances from all customer deployments around the world. These anonymized utterances from bank customers reflect real enterprise conversations and are used to continually train and optimize the EM&AI model. Banks automatically get the most current model ready to deploy, saving them significant time (months) and resources which would be required if they were to develop their own model on a generic platform. The EM&AI model provides tested performance and real-world usage out of the box.
Optimization is ongoing and never ending. It starts the moment we deploy the first testing environment. It continues to improve as real world utterances, content, and data is generated from usage. Throughout the customer subscription period, the EM&AI team will analyze the customer data, perform additional annotation, retrain the model, and evaluate model performance.
Additional information on EM&AI architecture and processes is available upon request. Contact firstname.lastname@example.org or your EM&AI sales person for more information.
EM&AI is a white-label technology that is fully customizable to support the brand and visual requirements of each bank. Additional customization can be achieved by using the SDKs to develop apps.
SECURITY & PRIVACY
Personally Identifiable Information (PII) is redacted before the data is stored. EM&AI only uses this anonymized data for annotation and training. The EM&AI model is not trained to identify individual users.
EM&AI does not store or use personal financial data received from the bank for any purpose other than answering an end user’s question. Once the question has been answered, the data is immediately discarded.
Here’s how it works:
- An end user asks the virtual assistant a question about their personal finance (“what is my balance?”)
- EM&AI makes a call to the bank’s backend system, with authentication, to retrieve data to answer the end user (access to the data must be permitted by the bank’s APIs)
- Once the answer is provided to the end user, the data is discarded from the system
EM&AI does not require user identity for general FAQs, product information, and any conversations that a bank agent would have with an unauthenticated user.
For authenticated or tailored exchanges, such as account balances or money movement, the bank must issue a temporary token to EM&AI to enable the use of the bank’s APIs, following standard security practices. The bank maintains full authorization control throughout the entire process.
EM&AI does not need the identification or password of the user. Only the bank will know which user maps to the temporary token, and is able to revoke a token at any time.
Additional authentication implementation information is available upon request. Contact email@example.com or your EM&AI sales person for more information.
EM&AI receives personal financial data from the bank in order to provide the end user with a response to a question that they’ve asked. For instance, when a user asks “What is my balance?”, EM&AI will retrieve the information from the bank with the user’s explicit permission. EM&AI does not keep a record of the user’s data and discards it once the response has been delivered to the end user.
The only data that may be partially retained is the text log within conversations. This is used for additional model training and is a common AI practice. The training is focused on natural language understanding and uses no financial-related fragments within the textual data. It is also anonymized and cannot be attributed to any individual.
No, banks do not have access to other bank’s data. Only authorized EM&AI personnel can access the pool of anonymized data used for annotation and training. The training model provides common improvements in understanding user utterances for general enterprise. Bank-specific information (product information, special terms, customized workflows and responses, etc.), is not shared and only exists within each bank’s virtual assistant.