India does not have the most financially independent population. A large percentage of the population here doesn't even have a bank account. So you can confidently say that financial inclusion is severely lacking in our country. Financial inclusion is when every individual in a country is provided with equal access to all financial services irrespective of their background. Artificial intelligence's role in financial inclusion is that it is a system with no biases and can be considered fair.

Conversational AI is a type of artificial intelligence that facilitates the human like conversation between a human and a software system in real time. It is a piece of software that a person can talk to, like chatbot, social messaging app, interactive agent, or smart device. These applications enable users to ask questions, get opinions, find support, or complete tasks remotely. Conversational systems are powered by a conversational engine named NLP (Natural Language Processing, a branch of AI that deals with linguistic and conversational cognitive science). They make use of large volumes of data processed with machine learning, and natural language processing to aid imitate human interactions, recognizing speech and text inputs and translating their meanings in different languages.

Conversational AI is a type of artificial intelligence that facilitates the human like conversation between a human and a software system in real time. It is a piece of software that a person can talk to, like chatbot, social messaging app, interactive agent, or smart device. These applications enable users to ask questions, get opinions, find support, or complete tasks remotely. Conversational systems are powered by a conversational engine named NLP (Natural Language Processing, a branch of AI that deals with linguistic and conversational cognitive science). They make use of large volumes of data processed with machine learning, and natural language processing to aid imitate human interactions, recognizing speech and text inputs and translating their meanings in different languages. Businesses can setup automated chatbots or virtual assistants that can communicate with humans via voice or text and in different languages of user preferences.

Certain systems powered by AI in smart cities may be expensive to incorporate or may need to comply with several regulations before implementation, making them'high-risk' AI applications. Using such risk criteria, here are some of the low-risk AI application areas for smart cities. Smart cities around the world are littered with advanced and interconnected technologies. The high level of automation and data flow allows such cities to optimize various public operations such as waste disposal and traffic regulation effectively. While the concept is still fairly new, several countries, like the US (New York, Santa Cruz), England (London, Manchester), and Spain (Madrid, Barcelona), include multiple highly automated and digitally interconnected cities today. Additionally, the smart city industry is expected to grow rapidly in the coming years due to its popularity and effectiveness in delivering the results expected from it.

There's no doubt that the finance industry is undergoing a transformational change. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.

In recent years, the banking industry has been at the forefront of AI and ML adoption. According to an Economist Intelligence Unit adoption study, 54% of banks and financial institutions with more than 5,000 employees have adopted AI. But AI and ML adoption has not been easy. Difficulty in deployment has been exacerbated by the growing number of new AI platforms, languages, frameworks, and hybrid compute infrastructure. Add to this the fact that models are being developed by staff in multiple business units and AI teams, making it difficult to ensure that the proper risk and regulatory controls and processes are enforced.

Attackers often look to take advantage of spikes in trends to launch attacks and trick innocent consumers into downloading malware or parting with sensitive, often financial, information. We saw it at the end of last year, when hackers took advantage of the increase in communication around Strong Customer Authentication (SCA) to steal credentials, as well as during Black Friday and Cyber Monday. Sadly, hackers are now jumping on the back of the widespread attention around the Coronavirus to try and bait victims into opening malicious attachments that they believe to be instructions around how to stay safe. Researchers at IBM X-Force have identified several campaigns where opening the attachment results in an Emotet downloader being installed silently in the background. Similarly, Kaspersky revealed that they've found "malicious pdf, mp4 and docx files disguised as documents relating to the newly discovered Coronavirus. The file names imply that they include virus protection instructions, current threat developments, and even virus detection techniques."

Call volumes into banks have gone up 10x during the pandemic. Wait times have been hours at the peak of the crisis. Chatbots and other forms of conversational AI have been deployed to help. Tearsheet's Sara Toth Stub recently wrote a story about where chatbots are useful and where they aren't. After a lot of hype, they are providing some real value but it will be years before the technology approaches human customer service reps. Sara Toth Stub is my guest today for a deep dive podcast to discuss what's happening inside the customer service queue in leading banks and financial institutions.

Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. Four months ago we launched our AI in Banking podcast where we covered some of the most critical topics related to AI adoption and implementation in banks and financial institutions each month. Our series was based on interviews with AI industry experts, many of whom also shared their valuable insights during our first comprehensive banking research project, the AI Vendor Scorecard and Capability Map. For the fifth month, we reached out to the research advisors who had a hands-on role in helping us with our research with the aim of having them speak directly to banking leaders to help them understand how to prepare for AI disruption in banking.

Finance and banking are one of the oldest industries in the world and are well-known for their legacy methods and "classic" approach towards business operations. But, as the world becomes digitized, more and more industries ramp up their processes in order to keep up with the changes. Time has come for the banks and financial institutions to adopt innovation and handle some of the processes over to the technologies like artificial intelligence. So what will innovative tools bring to the industry and how they will impact the state of finance and banking? The biggest problem that the financial industry faced was the overwhelming amount of paperwork and information to deal with.