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Aditi Saha

AI for finance

AI is impacting many industries, with new breakouts and interesting cases, from disease detection in healthcare to style transfer in digital art. It seems AI has something to offer for everyone.

In this article we want to talk about how AI is used in financial services and explore use cases related to this industry. AI is not new there, it is already being used by innovative companies in the sector as a competitive advantage, and the big players are realizing this and starting to adopt it more and more. We believe this trend is only going to grow and the impact of AI in finance is going to be massive.

AI in Banking

In banking, one big challenge is Anti-money laundering (AML). Right now the amount of manual and repetitive tasks for AML makes scalability and efficiency a big challenge. More automated AML solutions are key for the banking industry to scale.

With AI there’s real opportunity to reduce cost and increase reliability. A specific example is using natural language processing (NLP) and text mining for due diligence processes, and as well as models for transaction monitoring and fraud detection.

Another area with great potential for an AI shift is Know your customers (KYC) initiatives. By leveraging modern AI techniques companies can know their customers with far more detail, by using more data and transform it into more relevant and timely information. Compliance officers and investigators leverage the highlighted information obtained by models to make better decisions.

Improving Asset Management with AI

Here are some areas in asset management where stakeholders are starting to see AI as a strategic tool for optimizing their operations.

Customized offering:

Using AI in a similar way that e-commerce and retail companies do the investment profile of a client can be well determined. Content, product offering, and support can be tailored with AI to the liking of each client, enabling a more relevant experience and better engagement.

This relies heavily on new NLP techniques as well as clustering and collaborative filtering techniques. Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.

Alternative Alpha Generation:

Using smart approaches to market data and alternative data sources, AI helps in sorting out vast volumes of information and output relevant insights and high-value actions. With AI solutions, we’re helping companies like Allianz GI to get the most relevant information on time to make the smartest and timely decisions. The value generation for this is direct, having the most relevant information in time, and acting on it leads to better margins and fewer errors.

Automating internal processes:

This is as relevant for the financial industry as it is for many others. Companies are using AI and automation to improve the efficiency of their internal processes. The possibilities here are endless. Through the right experience and the right solutions, an AI team could analyze and understand dynamics and processes within a company, and propose changes to enable automation and the right use of AI tailored solutions. Increasing operations efficiency usually leads to a very impactful and sustained cost reduction.

AI in the Insurance sector

As in banking, the insurance business can benefit a lot from KYC solutions, as well as automation of internal processes. Here are some examples of the possibilities.

Managing Risk:

Compliance and risk management are traditionally bureaucratic and heavily manual tasks. We’re building AI-based solutions to enable brokers, partners, salespeople, and operators to filter out the noise and reduce their lower value activities by having the right information about their clients, the markets they operate in, and their assets.

As in other industries, here, AI solutions based on data analysis and even simple self-reviewing rules, can lead to the automation of repetitive tasks and a focus on high-value employee output.

Complementary to this, we see a huge opportunity in insurance for a more personalized offering, through the use of automated client profiles generated by a machine learning model.

Property insurance:

Insurtech is focusing a lot on AI-based solutions to old insurance problems. Tryolabs worked with a company to develop a novel approach for detecting roof damage in property with Computer vision and used that information for automatic underwriting.

Payment systems and analysis of transactions exploring AI solutions

This area is complimentary and very aligned with the offering of financial institutions. Up until now, there has been a much more recognized impact in retail and e-commerce, but the pricing strategy with artificial intelligence is something that many payment solutions could leverage.

Pricing processes automation and pricing optimization techniques use econometric science to create AI models. These take key pricing variables into account to define an automatic pricing strategy with dynamic prices. The algorithm can get real-time information on market prices for competing products, considering deals, seasonality, supply and that way model the optimal price for a given item.

This is another traditionally heavily manual and non scalable process, that if solved correctly, could turn out faster, more effective and less costly transactions. More importantly it will have a direct positive impact on profit.

The AI journey has already started for the financial industry and has brought to the surface all sorts of opportunities in repetitive, time-consuming, manual tasks.

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