AI in Finance
Picture source – Financial Times
The finance sector is starting to use artificial intelligence in a wide variety of ways[i]. As vast amounts of data are being collected in finance, many companies are able to harness machine learning and AI to remain innovative.
While AI is widespread in many areas of finance, it is still far from others, such as tax and estate planning, which require complex inputs and decision making. However, bolstering AI in these difficult areas using machine learning, Blockchain, and human intervention offers some potential for growth.
Some current applications include:
Tracking account activity
Using AI to analyse and understand how account holders are spending, investing and making their financial decisions, and continually analysing data to improve investment strategies and find answers to customer queries.
Advisory tools
Similar to B2C robo advisory platforms that leverage AI to automatically manage users’ portfolios, advisors can automate their clients’ portfolios to minimize human error while still offering a personal touch. Kensho introduced ‘Warren’ to helps advisors perform quantitative analyses on market data. ForwardLane uses AI powered by IBM Watson to offer financial advisors access to quantitative modelling and highly personalized investment advice generally only available to ultra-high net worth individuals.
Fraud detection
Multiple card issuers use AI to detect unusual spending activity. Feedzai uses AI combined with machine learning to analyze sets of big data created during a user’s online sessions to mitigate fraud associated with online account opening, payments, and ecommerce.
AI is already heavily leveraged for use in fraud detection. Combining it with Blockchain, which can provide an un-editable ledger of events, paired with AI’s ability to analyse large data sets in real time could make it even more powerful.
Underwriting
AI can help underwriters create a uniform metric that accurately identifies risk across borrowers. Aire.io leverages the power of AI to create and assign credit scores to thin credit file individuals. The lending industry has already transformed its underwriting practices from relying on large databases to now using AI to analyze large amounts of scattered, unstructured data. Because AI can analyse these data sets in real time, there’s great potential for both borrowers and lenders to benefit.
Regulatory compliance
Banks can use AI to quickly scan legal and regulatory text for compliance issues, and do so at scale. IpSoft’s Amelia is a customer service bot that helps banks maintain compliance in conversations with customers.
Relying on AI to scan for compliance issues instead of a team of employees helps avoid human error and allows financial institutions to quickly analyse multiple documents and practices. Because it removes human biases and error, AI has great potential in regulatory compliance.
Marketing
Marketers can better up-sell or cross-sell banking and finance products by using AI to identify and anticipate client needs. SBDA Group helps banks leverage their data using algorithms and machine learning to create targeted marketing campaigns for individual customers. Fintech companies have been using AI to draw conclusions from bank data for a few years now, but marketing is an area in which human input still adds a lot of value.
Customer service
Firms can leverage AI to identify which clients are at most risk of leaving a bank or advisor. Finn.ai, offers a white-labelled chat bot that integrates into existing messaging platforms such as Line, Facebook Messenger, Alexa, and even the bank’s web chat interface. Not all customer service matters can be solved by a chatbot, so Finn.ai offers a Talk to a Human button at the bottom of the chat interface.
Reporting tools
Advances in natural language processing (NLP), along with AI’s ability to analyse large data sets, have made it possible for banks to rely on software to automatically create and distribute reports. Narrative Science, for example, automates the creation of anti-money laundering reports and allows the bank to adjust for the tone of the writing.
AI has been used for a while to generate reports in the wealth management and compliance sectors. However, there is still room for natural language reporting to extend to other forms (such as inside of a chat interface) as well as to other sectors of finance.
Other notable examples include CollectAI, which automates debt collection, Comarch, and MoneyHub.
Market and technology drivers include:
AI Platforms – there are now a wide a range of options available, so finance companies will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. Hybrid solutions could include open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.
Open Data – The new EU payments directive (PSD2) compels banks to open up customer accounts data to third parties authorised by the customer. Access to richer datasets, which were previously a barrier to entry, creates opportunities for fintech to build better models, leading to more intelligent apps and services.
Data Security - Techniques such as differential privacy and homomorphic encryption will enable secure end-to-end data access for internal and external projects and faster innovation cycles.
AI Regulation – Banks and fintech companies will work alongside regulators to make clearer regulations that remove grey areas and speed up adoption and innovation. Some advanced techniques such as bias prevention, auditable and explainable of machine learning models will be essential for AI projects that move to production.
Decision-Making - Automation will continue to progress from simple rules-based systems to complex augmented and autonomous decision-making systems supported by AI. For example, the Connecticut-based hedge fund Bridgewater is seeking seeks to replace more managerial functions with AI. Traditional banks could start experimenting with the same concepts, initially with smaller projects in isolated business units.
Closer Startup Collaboration – Banks will gain a competitive advantage by innovating faster in partnership with fintech start-ups. It’s a learning experience for all, and with each iteration processes are streamlined to make future projects more effective. Fintech accelerators will continue to grow and play a key role in connecting banks with fintech start-ups. For example, Techstars Barclays Accelerator offers entrepreneurs unprecedented access not only to a world leading bank, but also to Techstars international mentor and investor relationships.
Since AI is pervasive throughout many sectors of fintech, it may not be too early for your company to begin searching for a Chief Artificial Intelligence Officer.
Useful additional reading – https://www.ft.com/content/b497a134-2d21-11e8-a34a-7e7563b0b0f4