NS&I: Customer Chatbot
Source: gov.uk (2025-02-27)

A chatbot used by customers of NS&I to obtain answers to product and service queries.

Tier 1 Information

1 - Name

NS&I Customer Chatbot

2 - Description

This tool is used by customers of NS&I to obtain answers to product and service queries that they may have.

The tool enables NS&I’s online customers to get rapid responses to queries without needing contact centre agents to intervene. This provides customers with quick and accurate answers and enables contact centre staff to focus on more complex queries.

3 - Website URL

Access to the tool can be found by the bottom right chat icon on the NS&I Website found here: https://www.nsandi.com/

Further information about the tool: https://www.genesys.com/en-sg/company/newsroom/announcements/genesys-launches-genesys-dx-the-next-chapter-in-customer-engagement

4 - Contact email

xxx@xxx.xx (mailto:xxx@xxx.xx)

Tier 2 - Owner and Responsibility

1.1 - Organisation or department

National Savings and Investments (NS&))

1.2 - Team

Enterprise Service Management

1.3 - Senior responsible owner

Head of Service Operations

1.4 - External supplier involvement

Yes

1.4.1 - External supplier

Genesys Sopra Steria

1.4.2 - Companies House Number

Genesys: xx-xxxx-xxxx Sopra Steria Limited: xx-xxxx-xxxx

1.4.3 - External supplier role

Genesys are the vendor of the tool and provided further support and consultancy. Sopra Steria are the service delivery partner who provide ongoing development, staffing, support and maintenance.

1.4.4 - Procurement procedure type

Specified and procured by our service delivery partner, Sopra Steria.

1.4.5 - Data access terms

N/A

Tier 2 - Description and Rationale

2.1 - Detailed description

Genesys DX uses Natural Language Processing (NLP) to understand customer queries. Key elements include: Intent Recognition: Identifies what the customer wants to achieve by mapping their input to predefined intents. Context Maintenance: Tracks the conversation history to ensure continuity in multi-turn conversations. - Powered by ML algorithms to improve over time: Supervised Learning: Initially trained on labelled data to classify intents and responses. Reinforcement Learning: Learns from customer interactions to refine performance. Semantic Matching: Maps customer queries to relevant knowledge base entries using vector embeddings. - Rule-Based Systems: For straightforward workflows, it applies decision trees or if-else conditions. AI-Augmented Routing: Directs complex issues to human agents based on topic, sentiment, or urgency analysis.

2.2 - Scope

The NS&I Chatbot using Genesys DX is used for scenarios including: Product and Service Information Support: Answering common general non-account specific customer queries via chatbots or live agents.

Scenarios the tool is NOT Designed For: Highly specialised support: Tasks requiring detailed product and service expertise require direct human intervention. Financial advice: The chatbot cannot offer NS&I customers advice on their financial circumstances or savings needs.

2.3 - Benefit

The Genesys DX chatbot allows NS&I too analyse customer behaviour in real time, proactively address queries, and provide targeted assistance through chatbots, which seamlessly transition to human agents for complex issues. This ensures consistent, streamlined experiences while reducing response times and increasing customer satisfaction. Additionally, the platform consolidates customer data to give agents a 360-degree view, enhancing their ability to deliver informed and empathetic service

2.4 - Previous process

Sopra Steria need to continally enhance contact centre productivity and improve NS&I’s customer experience. Retail customer service best practice is followed in the implementation of a chatbot

2.5 - Alternatives considered

Sopra Steria is NS&I’s incumbent partner for contact centre technology and operations. Their technology strategy is to buy, not build. Industry leading solutions were evaluated, resulting in the selection of Genesys DX as best fit

Tier 2 - Decision making Process

3.1 - Process integration

The tool opens up allowing customers to select pre-set questions about frequently asked questions whereby the tool will enable the customer to answer their questions in a preset way. The tool provides customers with information to help them identify which NS&I products best meet their needs, or how to fulfil their customer objectives when interacting with NS&I. The tool also provides the user with the ability to write a bespoke message at any point to the chatbot, this includes restarting the chatbot, speaking to an advisor. Once the user is satisfied that their question has been answered or that they have obtained the information they are seeking, they can either ask another question, close the application or speak to a customer service representative.

3.2 - Provided information

The tool provides the user with text responses and links guiding customers to information on the website, or to self-service journeys the customer can follow, having first authenticated their identity by logging in.

3.3 - Frequency and scale of usage

The tool is available 24/7 and handles 100k+ interactions per month

3.4 - Human decisions and review

The tool has undergone user testing to ensure that its outputs correspond to its training materials, and to ensure consistent tone of voice. Customers are responsible for ensuring the information provided back from the chatbot has answered their questions sufficiently otherwise they can speak to a human operator.

3.5 - Required training

The model was trained on NS&I’s public domain product and service information. It was further configured through conversation scripts, developed by Atos and Genesys. Contact centre agents are trained to intercede when requested by a customer or where required by the script. NS&I takes an assurance role in ensuring the process treats customers fairly and is compliant. The chatbot is prefaced by intro text outlining that it can only respond to generic queries and how to contact NS&I for specific help

3.6 - Appeals and review

The tool either prompts the customer to make a decision based on the information provided by leading them to a self-service journey which the customer makes a decision if their question is now answered or they initiate a subsequent offline operational process. There is no specific guidance on who to contact in the event of errors but it is assumed the customer would do this by the other contact channels.

Tier 2 - Tool Specification

4.1.1 - System architecture

https://help.mypurecloud.com/articles/key-concepts-of-dialog-engine-bot-flows-and-digital-bot-flows/

The Genesys Chatbot uses the Dialog Engine and uses natural language understanding (NLU)

Key components of the architecture include:

Data Ingestion: Data ingestion of the data held on the NS&I public facing website into the Cloud environment is conducted using the Genesys Architect tool.

The retrieved documents are processed using Genesys’ Digital Engine Bot Flows. Genesys Digital Engine Bot summarises the retrieved information and generates a user-friendly response, which is then presented to the user.

4.1.2 - Phase

Production

4.1.3 - Maintenance

The Tool supplier’s recommended updates are deployed and regularly updated. The NS&I contract with the service provider requires them to maintain a N-1 patching strategy (maintaining a software version that is one release behind the latest version) .

4.1.4 - Models
- Rule-Based Models Decision Trees; : Used for routing queries or determining next steps in automated workflows based on predefined rules. If-Else Logic: Supports basic chatbot functionality and customer interaction workflows. Session State Management: Tracks multi-turn conversations by adhering to predetermined conversational flows.
- Machine Learning Models Natural Language Processing (NLP) Models: Intent Recognition: Identifies user intent in messages by training on conversational datasets. Entity Recognition: Extracts structured data like names, dates, and account details from unstructured input. Sentiment Analysis: Determines customer sentiment to adjust tone or escalate interactions to human agents. Predictive Engagement: Analyses user behaviour on a website or app to predict and proactively address their needs. Reinforcement Learning Models: Continuously improves the bot’s responses based on customer feedback and success rates.
- Statistical/Mathematical Models Routing Optimization Models: Allocates interactions to the most suitable human agent or department based on efficiency metrics like wait time and skill matching. Customer Journey Analytics: Uses statistical techniques to map and analyse touchpoints across the user journey. Real-Time Analytics Models: Tracks metrics like average handling time, customer satisfaction, and query resolution rate to optimize performance.
- Hybrid Models AI-Augmented Human Assistance: Combines rule-based systems with NLP and machine learning for live agent support (e.g., suggesting responses or retrieving relevant knowledge base articles). Conversational AI: A hybrid of ML-driven NLP and rule-based fallback mechanisms for cases where intent is unclear.
Tier 2 - Model Specification

4.2.1 - Model name

Genesys Dialog Engine Bot

4.2.2 - Model version

01-2025

4.2.3 - Model task

Genesys Dialog Engine Bot Flows and Genesys Digital Bot Flows uses a natural language understanding (NLU) engine that can interpret and process information the customer provides as input, to provide a output answer including guidance were possible.

4.2.4 - Model input

Text-based customer questions in English

4.2.5 - Model output

Relevant guidance based on NS&I’s public domain product and services information and terms of service.

4.2.6 - Model architecture

Natural Language processing (NLP) Dialog Engine uses the following key actions Intents: The intention of the user. What is the user trying to do? Utterances: What would the user say to convey their intent? Slots and Slot Types: What values can the bot infer from the user’s utterance. What is the specific piece of information that the bot would map to an entity? Confirmations: A message sent by the bot to confirm that it understands the user’s intent.

4.2.7 - Model performance

Prior to release - User acceptance testing for accuracy by subject matter experts. Customer acceptance testing for usability and tone of voice.

4.2.8 - Datasets

NS&I’s public domain product and services information and terms of service

4.2.9 - Dataset purposes

Training the model required ingestion of the documentation in suitable formats. On completion, the model provides relevant responses to NS&I customer queries

Tier 2 - Data Specification

4.3.1 - Source data name

NS&I’s public domain product and services information and terms of service

4.3.2 - Data modality

Text

4.3.3 - Data description

Webpage name, webpage content. PDF name; PDF content.

4.3.4 - Data quantities

A knowledgebase containing circa 200 web pages and document links.

4.3.5 - Sensitive attributes

N/A - all information is public domain.

4.3.6 - Data completeness and representativeness

The data includes all NS&I’s public domain product and service information which is factual and correct. Questions not answerable by the chatbot from these sources may prompt a human agent intervention.

4.3.7 - Source data URL

https://nsandi.com

4.3.8 - Data collection

NS&I gathered together all of its NS&I’s public domain product and service information. As this data is another form of publishing the same information to the same audience, the purpose is identical.

4.3.9 - Data cleaning

N/A - Not required.

4.3.10 - Data sharing agreements

N/A - the data is public domain.

4.3.11 - Data access and storage

NS&I and Atos staff have access to this dataset alongside NS&I customers and the general public due to the information being publicly available. As mandated by NS&I’s knowledge management and data retention policies, information assets are subject to regular review. Additionally, there are ad-hoc reviews of documentation as required when product and service terms change.

Tier 2 - Risks, Mitigations and Impact Assessments

5.1 - Impact assessment

A DPIA assessment was undertaken and confirmed that this tool contains and collects no Personally Identifiable Information. The tool requests no personal information from the customer, other than first name should they wish to speak to an advisor

5.2 - Risks and mitigations

The main risks are:
- data becoming out of date, and
- inaccurate outputs.
How are we mitigating against these risks: - User and customer testing to assure chats are delivered in accordance with the scripts and sources - As mandated by NS&I’s knowledge management and data retention policies, information assets are subject to regular review. Additionally, there are ad-hoc reviews as required when product and service terms change.