It's been over a decade since AI has pervaded into almost every aspect of our daily lives, yet some still persist in assuming the rise of AI as a dystopian scenario in which robots have overtaken the world.
The power which AI presently holds is far more mild yet intricate. AI has sunk its paws down the product recommendations offered to us by Amazon, the series suggested to us courtesy of Netflix or those infuriating spam emails that get “magically filtered”.
Speaking of Amazon, you can check out our blog on Big Data in Amazon
With AI around, routine tasks have to a certain extent been overtaken by technology, revolutionizing the manner in which work is handled across the organization. Finance is no exception, with tech solutions being made the base for many of its operations. No one has the time or the patience for handling manual reviews and the hazard presented by faulty data in their activities.
From allowing for flawless, 24/7 consumer interactions, minimizing the requirement for repetitive tasks, cutting down on human errors and false positives, saving money, automating tasks, fraud detection, and offering personalized suggestions, there’s no limit to the perks AI has offered to this industry.
Document capture technologies allow financial institutions to automate their evaluation procedures of credit applicants.
Why stick to the taxing process of reviewing payslips, invoices, and other financial documents in a manual way, when the task can simply be handed over to AI algorithms that can flawlessly take charge of these operations, automatically capture document data and handle lending operations with minimal human involvement?
This would inevitably allow banks and financial institutions to wrap up credit applications swiftly and with slighter errors.
Likewise, suitable data can be captured by financial organizations via cash flow statements and other financial documents of the borrower companies. The extracted data allows banks to offer speedy services for their lending operations, while also enabling more accurate handling of credit evaluation.
Credit applications can be leveraged swiftly and precisely by Financial companies by making use of AI. Predictive models are leveraged by AI tools for examining the credit scores of applicants and allow for minimal regulatory expenses and compliance and improved decision making.
Suitable financial information can be examined through AI and insights regarding financials can be offered to make use of techniques such as machine learning. Rather than indulging in the tedious process of executing numerous calculations using spreadsheets or financial documents, all these massive volumes of documents can be handled and insights can be derived without anything being missed out. This allows for improved commercial loan decisions.
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One lucrative example of AI being applied in the lending segment is that of the startup Lenddo. Established in 2011, Lenddo fixates over emerging markets in which the rising middle classes generally do not have conventional credit histories or bank accounts.
The platform is making a thorough adoption of advanced machine learning for sifting through the massive collection of data to predict an individual’s creditworthiness. This unconventional data is derived from the applicant’s social and psychometric data and their online behavior.
The platform boasts of aiding millions of applicants with little to no credit history gain better financial inclusion. By allowing the users to download their app, Lenddo observes the complete digital footprint of the applicant to decide on their creditworthiness.
The platform claims to examine more than 12,000 variables that include social media account use, internet browsing, geolocation data, as well as other smartphone information. Its machine learning algorithm converts this data into a credit score which can be adopted by banks and other lenders.
Virtual assistants and AI chatbots can be leveraged for monitoring personal finances. Insights derived through the spending amounts and target savings can be offered by these assistants.
Financial advice for helping investors in managing their portfolio and to suggest a customized investment portfolio consisting of bonds, shares, and other such assets can also be offered by Robo advisors apart from offering insights solely on personal finances. Customer’s information regarding their risk appetite and investment experience is used for the same.
Banks and other financial institutions make use of AI for resolving the issue of delinquency and offer a proper and efficient procedure for debt collection.
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An example of AI in debt collection is TrueAccord. Established in 2013, San Francisco, the platform claims to be offering AI-driven debt collection solutions to banks, eCommerce, and telecom companies. The platform claims that their decision engine makes use of machine learning for developing digital interactive experiences, personalized for every debtor.
Invoice capture tech powered by AI can be introduced by financial institutions for automating their invoice systems and adopting accessible billing services to remind their consumers to pay. This will in turn allow businesses to speed up their processes, minimize any manual errors and expenses, while also boosting loan recovery ratios.
AI can be adopted by companies to derive data through bank statements and to compare it in intricate spreadsheets. AI allows the account reconciliation procedure to considerably expedite and for any errors, hindering the process to be eradicated.
The areas where AI is applied in Finance
Similar to credit applications, AI is capable of assessing the risk profile of consumers and determining the best possible prices to accompany the appropriate insurance plan. This cuts down on costs, minimizes the business operation workflow and also boosts customer satisfaction.
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Multiple tasks such as review, adjustment, investigation, and remittance are a part of the procedure of claims processing. Since AI can manage a massive number of documents indispensable to these tasks, document processing technologies, also allowing it to detect any fraudulent claims and to ensure that the claims adhere to the regulations.
For instance, Tractable has launched an AI system that can detect accident images and deduce the repair expenses as well. The platform claims that insurance companies are capable of accelerating claims processing by ten times.
Yet another example of AI in Insurance is that of Cape Analytics. This is a computer vision startup that revamps geospatial data into actionable insights to help insurers in making improved policies and offering proper propositions to aid homeowners in saving their property from wildfire damage. The startup makes use of AI for producing thorough data relating to how near the surrounding structures are, the density of vegetation, the roof material as well as the calculated risk that homeowners can make use of for taking preventative action.
Cyber and data breaches are one of the primary challenges faced by banks in today’s times according to KPMG. As per its survey, over half the respondents reveal that they are able to reclaim below 25% of fraud losses, making fraud prevention an indispensable task.
AI technologies have advanced significantly to keep track of fraudulent actions and handle system security. AI adoption in the Adopting AI for fraud detection can also boost general regulatory compliance matters, minimize the workload, and operational expenses by cutting down on being exposed to fraudulent documents.
American Express, for instance, adopts fraud algorithms optimized with NVIDIA TensorRT for monitoring each transaction on their platform in real-time for over $1.2 trillion spent annually. The platform has leveraged deep-learning-based models for detecting fraud and generating decisions within the blink of an eye.
Abiding by the regulatory specifications is integral for all financial institutions. NLP tech can be adopted by AI for scanning regulatory and legal documents to detect any compliance issues. This makes it a broad and effective solution in terms of cost since it allows AI to browse through numerous documents swiftly to oversee non-compliant issues in the absence of any manual involvement.
The travel receipt checks are required by expenditure reports for a range of purposes ranging from income tax laws, compliance, as well as VAT deduction regulations. This poses many compliance risks in relation to fraud and payroll taxation.
AI can make use of deep learning algorithms and document capture technologies for preventing non-compliant spending and minimizing approval workflows.
With the adoption of AI technologies such as NLP, it becomes possible for banks to detect any irregular patterns and determine risk areas in their KYC processes in the absence of human involvement.
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In extreme circumstances calling for human interaction, the case is swiftly forwarded for approval. Lesser errors, a boost in security and compliance, and faster processing, there are numerous perks the application of AI offers in KYC.
The requests of consumers can promptly be addressed through Conversational AI systems, human intervention is only required in case of nonresolution. With customer requests being handled swiftly through AI, call center workers can fixate their attention on more complex requests.
Square, a digital payments fintech, makes use of conversational AI to dominate its virtual assistant, which comprehends and offers aid to about 75% of the consumer queries, minimizes any no-show appointments from potential consumers with the sales team by around 10%.
The unresolved and non undertaken consumer queries can be identified by banks and other financial institutions through AI technologies as well as CRM systems. This in turn helps in enhancing both revenues and also the customer satisfaction. For instance, a platform can suggest car insurance for a customer who is amidst the process of purchasing a car.
The AI models are capable of determining patterns in consumer behaviors and also predicting which consumers have a better probability of churning in the next term. By observing these behaviors, the financial institutions can determine why a consumer is at risk and take appropriate actions for preventing churn.
Financial firms and markets don’t just rely on the tendency and opinions of current investors. Rather, what is given priority is the future analysis and trends to these platforms which trade on varying aspects of the market and benefit from its erratic nature.
AI aids these financial firms in ensuring returns to a certain extent, against the organization graphs and market patterns which they examine from all the data gained over the years. The software tools can also be adopted by Governments to ensure more efficient policy-making.
A set of solutions can be recommended by AI to satisfy the requirements of every person. People with a high-risk appetite can depend on AI to decide on when they wish to purchase, hold and sell a stock, while people having a low-risk appetite can obtain alerts notifying them when the market has the scope of falling so they can determine whether they wish to remain invested in the market or to exit it.
We’re long past the stage when the inclusion or exclusion of AI in the field of Finance used to be subject to debate. AI has revamped the finance sector into a fluent and precise machine that derives its insights via data, analytics, and accurate patterns. AI is definitely here to stay and it will be interesting to observe how the technology further sinks its paws into the sector.
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