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Artificially Intelligent Finance

Artificially Intelligent Finance


Artificially Intelligent Finance

The traditional world of finance is undergoing a revolution.

 Fintech refers to technology solutions for re-inventing traditional finance. The word like most buzz words after the dot com boom has become an entire industry worth more than  $3 trillion. More than ever before the leaders in finance are becoming technology adopters With Gartner predicting that most financial service providers will become technology providers of the future. How possible? As customers demand more speed and accuracy, financial institutions must offer better services that meet this demand. Innovative technology solutions that keep them as market leaders and companies of choice.  The Technology department of most banks, insurance companies and security exchanges are getting bigger than core operations. We believe this is happening because they are hiring more software engineers, IT infrastructure managers, Data Analysts,  cyber security experts (to wade of cyber attackers), big ticket CIOs to improve operation efficiency or innovate for the consumer space.

Cybersecurity and the new threats to global finance

With cyber theft and online marauders becoming such big threats for corporations and now nations, investment in technology to drive the fintech industry is increasing now more than in the past two decades. Issues may range from theft of user passwords that cost a few thousands of dollars to major breaches in firewalls and sensitive data that is worth billions of dollars. Increasing incidences have ensured that the Chief (information) Security officers of large banks call for stricter measures in user authentication and customer sensitization. In a recent study where more than 254 companies surveyed globally, it was discovered that financial institutions suffer an average of 125 intrusions a year each costing an average of $900,000. The paradox of this is that as we look for more advanced ways to serve the customers better to create ease of service and access, the more we create room for new threat. Researchers have argued that this is a necessary evil. Many of them positing that we cannot deny consumers new and improved service because we are afraid of Malicious attacks. While examining my thesis for my graduate research, I considered how social attacks is now made possible through social media. The closer financial services (loans, retail banking and investment) come close to social media, the more the need to massively invest in advanced technology to protect user information from malicious users. This is the fuss about cyber security. The field of cyber security is greatly influenced by our knowledge of computers and human behavior. It is about how we study patterns. These patterns allow system engineers to design software that can identify pattern through a methodology called pattern recognition. To make this systems efficient, system engineers must automate the learning process and enable machines do the same, allowing software to learn the patterns and predict more complex scenarios than humans would ever do. This deep neural networks can detect forgery by imitating and creating deep fakes in digital signatures and testing themselves with this deep fakes to gain strong resistance against future attempts by humans or other malicious software.

The Market Size

In 2018, the International Data Corporation IDC estimated the investment of financial institutions in the US in AI systems at about $4Billion. This is an isolated statistics of the US alone, without considering all other investment in technology. The IDC further postulates that this spending may go beyond $77Billion by the year 2022. That is a huge market any software company looking to select a niche should consider. Some of the popular use cases of AI in Fintech, include Insurance underwriting, debt collection, fraud and crime detection and credit scoring. Anyone would agree these are fundamental operation areas for many finance practitioners. AI is being used for processing claims in the insurance sector, identifying malicious patterns and building new resilience, determining who banks should lend to, and who might have difficulty paying back.  Some more mainstream ways business in the Finance space apply Artificial Intelligence is in Customer Service management. Advancements in Natural Language processing and cognitive services have made many off the shelf customer service tools available at affordable costs. Some financial services companies now invest in building their own chatbots to get closer to customers. Chat bots allow individuals to converse with natural language with a computer with a body of knowledge and perform simple or complex task. Advance use of AI will be in Algorithmic trading and business intelligence.  

At the core of it most of the problems AI seeks to solve in finance are Big Data Problems. How do we collectively analyze millions of lenders,  their assets, collaterals and spending patterns over a period of time to make tiny improvements that can save us millions of dollars. Lending is founded on the credit worthiness. AI is helping banks and other companies offering loans to do this faster. Determining the applicant’s credit score analyzing thousands of data points for those with past credit history and using what is called alternative data for new lenders. Alternative data is founded on an applicants digital footprint or social capital. The use of alternative data has helped companies like Lenddo, ZestFinance and Social Lender to help hundreds of thousands access loan without credit history and lenders approve 50% more applicants. The three companies all claim to use AI to analyse over 10,000 data points from an applicant’s social presence, geo location, smart phone data to decide who is credit worthy and who is not.

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