Top 6 Use Cases / Applications of Deep Learning in Finance in '24
Banking will be one of industries that will spend the most on AI solutions by 2024 according to IDC. Banking sector is expected to focus on making investments in fraud analysis & investigation, recommendation systems and program advisors. According to Accenture research, AI solutions will add more than $1 billion in value to the financial services industry by 2035.
Deep neural networks (i.e. deep learning) provide capabilities to automate complex operations and decisions at higher degrees of accuracy compared to other approaches. However, the volume and quality of trained datasets are critical for deep learning networks to produce better and more accurate insights. Data which financial companies have – such as transactions, payments, bills, suppliers, customers – gives an opportunity to develop effective deep learning solutions.
Why is deep learning relevant in finance?
Finance deals with both structured and unstructured data such as documents and text. Deep learning allows financial firms
- to convert unstructured data into structured, machine readable data. For example, this allows banks to get financial information on companies from their annual reports published in regulatory platforms like the Companies House in the UK
- to make predictions & classifications on structured data. Data such as stock market information is highly structured and can be used to automate trading activities
Financial services companies use finance-specific chatbots with deep learning models to improve user experience. Deep learning based solutions bring personalized services to customers. According to customer’s financial activities, virtual assistants can
- automate frequently completed actions
- suggest products that are not used by the customers but can be a good fit for them
- answer questions
Deep learning algorithms can identify potential churn by analyzing interactions. This capability helps insurance/insurtech companies and banks to offer discounts and new plans and protect their customer base.
Financial Security and Compliance
Deep learning algorithms are effective for
- revealing suspicious transactions with high precision in real time
- Using unstructured data (e.g. satellite and street view images) to check the existence of a business or to perform other compliance controls
This provides advantages such as
- reduction of operational cost
- improvement of regulatory compliance
Insurance companies use historical consumer data to train deep learning algorithms. This consumer data includes health records, information gathered from wearable devices, potential health issues, age, income, profession, loan payment history, etc. Deep learning based solutions help sector to
- predict and reduce risks
- set suitable premiums
- improve speed and accuracy of underwriting processes.
To learn more, you can check our article on how AI improves underwriting processes.
Thanks to computer vision and document processing capabilities, deep learning models allow insurance companies to asses damages for car accident claims and risks for home insurance. Also, these models can identify fraudulent claims more accurately.
Deep learning models use learned patterns and results of document processing to assess credit risks and loan requests. This data covers income, occupation, age, current financial assets, current credit scores, overdrafts, outstanding balance, foreclosures, loan payments. Then, they can make a decision about the qualification of the client for lending.
By analyzing historical data & current price movements and extraction information from the news simultaneously, deep learning algorithms can predict stock values more accurately. These predictions are used for fast trading decisions. Due to lack of emotions, predictions and decisions deep learning models deliver are more neutral/objective and data- driven.
For more, feel free to read our comprehensive list of AI use cases in finance.
Banks are traditionally risk-averse institutions since they have suffered significantly in times of financial crises when risky bets led to bank failures. For example, in the area of lending, this has led to the boom of fintechs focused on lending like SoFi.
In addition, banks and insurers are highly regulated institutions and need to be able to show that for example their lending or underwriting decisions do not exhibit bias.
Therefore, deep learning’s challenges (i.e. hard to explain predictions) pose unique challenges for banks.
If you are ready to use deep learning in your 66firm, we prepared a data driven list of companies offering deep learning platforms.
For more on how technology is transforming financial services, learn about
If you need help in choosing among deep learning vendors who can help you get started, let us know:
This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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