Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. For businesses, Cloud-Evolution provides the AI technologies they have to change their business processes and workflows while also substantially increasing productivity.
AI Helps in
- To transform into a digital company, data modernization is essential
- Faster and more intelligent decision-making
Computer science & artificial intelligence (AI) include a section called machine learning that concentrates on using data & algorithms to mimic how people learn, progressively increasing the quality of its predictions. Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine Learning Helps in
- Rather of spending time gathering data as well as creating presentations, focus on making confident decisions.
- Natural-language processing may make it easier for people to find important information.
- Machine learning may uncover insights that were previously hidden.
- Investigate possible future actions based on the current situation.
Frameworks
Do you know and understand or know your customer well enough?
Based on United Nations Office $100B spent on Money Laundering and financial compliance and $15-$20B on Software and analytics
Inspite of these spending, companies are penalized with $35B for noncompliance. The reasons are many as Traditional rule based and Statistical approaches have proved inadequate, slow, laborious, and Expensive. To understand and characterize human behavior.
Intelligent segmentation of people and behavior effects in the positive way for Anti-money laundering (AML) and Know Your Customer(KYC) Outcomes. Artificial Intelligence and machine Learning makes this possible with a little impact on existing system at both agile and scale.
Intelligent segmentation across its life-cycle that is beyond customer knowledge and diligence level is key to attack Money Laundering threat. Artificial Intelligence and Machine Learning plays important role.
What can we do?
Layer the trusted data from different sources to effectively streamline and increase the visibility on risk, based on behavior through the Know Your Customer/AML process. AI and ML in onboarding stage of KYC process add Immense value. For Example, Image recognition features can verify the documents match by scanning different sources to evaluate the customer KYC risk Score.
How can we do it?
Platform to manage customer data (Customer Data Management Platform) is the key to personify your customer and should be able to execute following four functions
- Ingesting audience data from multiple sources by Stitching together and integrating with other databases like, marketing, and or ecommerce engines.
- Managing customer identities.Which includes reconciliation of email ids and Phones Effectively, with what the brand knows about customers before they share their data essentially a cross-device identification.
- Supporting real-time customer segmentation.CDPs allow marketers to build and maintain universal, omnichannel audience segments of customers with the unified data available in the customer data platform.
- Data provision and activation. Once customer profiles have been unified, the data must be available in real-time so brands can deliver relevant, personalized interactions. This is done through providing connectors and APIs to other marketing technologies.
Framework
Extract Customer data from various sources can be transferred to onboarding system while the customer data is unified and segmented. During this process a self-learning solution can create a dynamic questionnaire to evaluate customer identify. Then it adapts the response from the customer and map to intelligence gathered from different sources. Add-on based on mapping chatbots can be deployed to seek additional KYC Documents. Using Sophisticated clustering technique for example, Can group individuals by multiple parameters for highly reliable KYC risk assessment. This information can be used to screen money laundering risk signals, match identities, and identify complex money laundering patterns and provide early alerts. AI and ML conduct real time analysis on transactions, their history and on multiple unstructured data source (Swift messages and anomalies in Invoice addresses)
At the operational level, combination of RPA(Robotic Process Automation), Natural language Processing and cognitive computing is potential positive factor to onboard a customer.
RPA can make onboarding process easier by capturing data from various sources including KYC documents using industry accepted techniques. This data can be compared with data provided by customer and any discrepancies can be flagged. Fact that KYC and AML are extremely data intensive and makes them more suitable for RPA. Whether it is automating manual process or discovering suspicious banking transactions, RPA implementation proved instrumental in terms of saving both time and cost when compared tradition banking solutions.
Business Demands a single Platform to orchestrate the entire risk lifecycle
Artificial intelligence is making remarkable developments in the data analytics domain due to dynamic data processing and validating capabilities. Businesses are beginning to capitalize on AI’s data extraction and processing capabilities to ingest value-based automation in critical KYC processes, such as-
- Data Extraction and Verification
- Fraud Detection, and
- Anti-money laundering Compliance
Artificial intelligence services for process accelerates and enhances customer experience.
Conversational Artificial Intelligence
2020 has seen a great boom in the use of chatbots across the industrial sector. Chatbots are full of advantages to provide undeniable experience for customer services. So, conversational AI is an integral part of business practices across all industries.
The role of chatbots is not limited to customer services only. Although they are recommended for customer support. Conversational Bots are helpful to promote marketing and sales activities.
Since chatbots are a small application of conversational AI. Conversational Intelligence has a much wider scope. It compiles the trending technologies and utilizing the mix for variant purposes. Artificial Intelligence has captured a significant share in conversational marketing.
“What” & “Why” paves a path towards the Conversational AI trends for 2021. And, also, to the reason why it is a priority for the major section of the industry.
Conversational AI encompasses technologies such as voice assistants and chatbots capable of responding to natural language and offering human-like interaction. They use machine learning, advanced dialogue management, natural language processing, and automated speech recognition to process text and voice input and learn from interactions.
Why do we need Conversational AI?
The systems and processes are getting more and more complex with increasing technical demands in the industry. Navigating through the complex problems with little or no assistance from an expert is an uphill task and it gets difficult for the non-technical users to. The use of chatbots at the forefront eases the movement of the user when surfing or shopping online. It is a step ahead towards betterment. Customer experience is the priority for the industries now. Hence, the customer experience will be simpler and more intuitiv
How Conversational AI works?
Machine Learning
Machine learning enables systems and applications to learn from experience by processing large volumes of data and identifying patterns. Different algorithmic approaches have been tested over the years, from reinforcement training and clustering to inductive logic programming and decision tree making to enable the software to learn with minimum intervention.
Natural Language Processing
A branch of artificial intelligence, Natural Language Processing (NLP), aims to empower machines to understand and manipulate human language. Based on machine and deep learning and rule-based modeling, NLP enables software agents to process and summarize text and interact with humans through natural language generation, sentiment analysis, word sense disambiguation, and speech recognition.
Conversational AI is a technology behind automated messaging and speech-enable applications (like FAQs, Amazon Alexa, Siri.) It can communicate like a human by speech recognition and text, understanding intent, decoding messages, and responding in such way like human talk. AI chatbots use machine learning to understand user input and hold a conversation in a human-like way.
Putting puzzle pieces together…
Conversational AI works in a Pattern. Information received from human is deciphered to text or speech. Using Natural Language processing it understands the human intent. Then its responds to human intent based on its understanding using Dialog Management. Dialog management automate the responses and converts then into human understandable format using Natural language generation. Then it delivers the response in text or speech. Machine Learning enable the system to keep updating it understanding of human behavior, language.
Use Cases
Business uses Conversational AI for marketing, sales, and support to engage along their entire customer journey.
Finally
Conversational AI doesn’t depend on manually written scripts like chatbots. There were powered by Deep Machine Learning which enable easy scalability. It well understands a wide variety of questions without being explicitly trained. The more advanced Conversational AI can analyze and identify customer questions and issues to identify common tricky points to respond before even customer reaches out. In this system, AI not only drives the Conversation but also enable sales specialist to focus on more value-added work. A Conversational AI should lever below to significantly improve both performance and customer experience during sales.