Thanks to advances in machine learning and its integration in modern business software, even the minutest of business operations can be automated today.
For the professional services industry, artificial intelligence along with good data management practices unlock a variety of powerful use-cases. This is even more so because professional services are primarily knowledge-based expertise businesses. The fundamental unit of value that underlies all of the knowledge work is data (and the transformation of data to useful knowledge and insights).
The professional services industry, therefore, much like any industry that is heavily reliant on the use and transformation of data, is as a rule of thumb, ripe for disruption by artificial intelligence.
In this article, we consider AI’s use cases in three such professional services: banking, accounting, and healthcare.
Overview
When it comes to professions like banking, accounting, and healthcare, artificial intelligence holds the key to efficiency in almost every process (such as data abstraction, categorization, information processing, storage of data, faster retrieval, analysis, document automation).
Similarly, Artificial Intelligence has various applications for these professionals in areas of risk management, decision-making, and insight gathering. Not only does this result in mitigation of various types of risks (think of fraud detection, prevention, risk profiling, predicting financial irregularities, or early detection of terminal diseases).
Artificial Intelligence in Banking
The banking sector has been undergoing a massive disruption since the financial crisis of 2008. FinTech (financial technologies) and NeoBanking companies are now becoming commonplace; they have set new industry standards in banking and have brought new business models. There are various structural forces at play when it comes to the industry dynamics of the banking industry, which though beyond the scope of the article, is nonetheless worth noting because technology has been at the core of the disruption in the banking industry.
Artificial intelligence, in turn, plays a central role for bankers in modernizing their workflow, automating repetitive tasks, which lead to efficiency gains.
Artificial intelligence is also central in bringing resiliency gains since the use of AI can help bankers detect, prevent, and handle frauds and customer grievances with far more accuracy and precision than humans have traditionally been able to handle.
Customer behavior analysis
The first area where banks can use AI technologies is to identify customer transaction patterns, behavior, and digital footprint. While these tasks were either impossible or significantly difficult to achieve manually, AI can automatically track customer behavior and transactions to identify anomalies and abnormalities.
Fraud prevention
Banking consumers today have access to a variety of payment methods, instruments, and channels. Today, banking transactions are done with the help of many intermediary parties who process transactions in real-time. This, in a sense, is a new “system” in payments processing. However, as John Gall has pointed out in his famous work on systems theory: new systems present new (and far more complex) problems. Similarly, the multiple payment systems that exist today have brought in new forms of fraud, money laundering, and theft.
The advantage of using Artificial Intelligence in this context is massive and irreplaceable. Cyber frauds do have a tendency to use new and sophisticated means, which creates permanent problems for human experts to keep learning and installing combative measures. Since AI-based expert systems have the ability to learn based on the data presented to them, fraud detection and prevention is an area where AI can deeper data insights to prevent fraud detection. With AI, banks can easily gather and integrate customer data, and analyze it to identify the hidden patterns quickly.
Credit assessment and risk profiling
Further, banks can use AI algorithms for credit assessment and risk profiling while disbursing loans. This way, they can minimize their NPA litigation, reduce credit risks and mitigate the chances of loan frauds significantly. They can also AI to track consumer patterns and target more tailor-made loans as per the customer’s preference and repayment abilities.
Prevention of phishing activities
Artificial Intelligence can also help banks in identifying and preventing phishing frauds. Banks can use domain generation algorithms to track fraudulent websites, identify suspicious web traffic, phishing websites, etc. This can help them provide a safer banking environment to their customers and prevent their cyber-crime-related legal matters substantially.
Chargeback
AI can also help banks reduce their chargeback-related legal disputes with customers. Banks can automate their chargeback processes by using AI and removing the redundancies that come with the reconciliation and decisioning involved. Not only can this reduce the time taken to resolve chargeback disputes, but also decrease fraud chargeback cases for the banks.
AI for Chartered Accountants
CAs deal with mammoth volumes of financial and non-financial data. To execute their services without error, this data must be accurate, consistent and obtained promptly. Even a single inconsistency or delay in such data transmission can jeopardise a client’s interest.
Automation of most accounting activities
In an industry where accuracy of data is so critical, artificial intelligence based systems can interpret, categorise, segment, tag, and contextualise large swathes of data, and then bring to the human expert who can make decisions on the basis of the analysis. A large chunk of accounting activities can in fact be automated from start to finish, with insights and analysis included, without the intervention of human experts at any point in the process. Examples of these are review of financial statements, returns, books of accounts and other financial information of clients. This can further allow them to reduce data inaccuracy and mitigate the chances of legal risks for their customers.
Tracking compliance requirements
AI can also help CAs to handle the accounting functions of clients operating in different jurisdictions and regulations. AI-based algorithms can be used to parse different sources of data containing laws, regulations, and rules, which can then bring filtered results to accountants. Tracking regulatory compliance changes can therefore be automated to a large extent.
Auditing
Auditors can also use AI for contract analysis, identify contingencies in an organisation and perform financial analysis easily. Not only can this reduce the administrative costs associated with document review by auditors, but it also allows them to accurately create their audit reports and outcomes.
Financial irregularities
Fraud detection is another area where AI can help chartered accountants. With improved predictive models based on machine learning, CAs can easily detect financial aberrations. CAs can also go through non-financial data such as contracts, emails, etc., to identify deviations and carry out more in-depth investigations.
Artificial Intelligence for Healthcare Professionals
The application of artificial intelligence in healthcare has led to an evident shift in the way hospitals/doctors/medical practitioners. AI is creating a ‘do it yourself’ ecosystem for consumers with self-help tools for well-being and disease monitoring.
Healthcare professionals can now offer more precise and personalised solutions to their patients based on their specific ailments and diagnosis. With AI tools, medical professionals can automate tasks such as prescription renewals, report analysis and suggest accurate treatments to patients.
The use of AI chatbots can also help in the detection of diseases based on the symptoms prescribed by the patients. In all, artificial intelligence can help medical professionals reduce the chances of misdiagnosis and medical negligence, along with the legal repercussions associated thereof.
