AI and machine learning are already being applied in the front office of financial institutions. Large-scale client data are fed into new algorithms to assess credit quality and thus to price loan contracts. Similarly, such data can help assess risks for selling and pricing insurance policies. Finally, client interactions may increasingly be carried out by AI interfaces with so-called ‘chatbots,’ or virtual assistance programs that interact with users in natural language.
Credit scoring applications
Credit scoring tools that use machine learning are designed to speed up lending decisions, while potentially limiting incremental risk.
Lenders have long relied on credit scores to make lending decisions for firms and retail clients. Data on transaction and payment history from financial institutions historically served as the foundation of most credit scoring models. These models use tools such as regression, decision trees, and statistical analysis to generate a credit score using limited amounts of structured data.
However, banks and other lenders are increasingly turning to additional, unstructured and semi-structured data sources, including social media activity, mobile phone use and text message activity, to capture a more nuanced view of creditworthiness, and improve the rating accuracy of loans. Applying machine learning algorithms to this constellation of new data has enabled assessment of qualitative factors such as consumption behavior and willingness to pay. The ability to leverage additional data on such measures allows for greater, faster, and cheaper segmentation of borrower quality and ultimately leads to a quicker credit decision. However, the use of personal data raises other policy issues, including those related to data privacy and data protections.
In addition to facilitating a potentially more precise, segmented assessment of creditworthiness, the use of machine learning algorithms in credit scoring may help enable greater access to credit. In traditional credit scoring models used in some markets, a potential borrower must have a sufficient amount of historical credit information available to be considered ‘scorable.’ In the absence of this information, a credit score cannot be generated, and a potentially creditworthy borrower is often unable to obtain credit and build a credit history.
With the use of alternative data sources and the application of machine learning algorithms to help develop an assessment of ability and willingness to repay, lenders may be able to arrive at credit decisions that previously would have been impossible. While this trend may benefit economies with shallow credit markets, it could lead to non-sustainable increases in credit outstanding in countries with deep credit markets.More generally, it has not yet been proved that machine learning-based credit scoring models outperform traditional ones for assessing creditworthiness.
Over the past several years, a host of FinTech start-up companies targeting customers not traditionally served by banks have emerged. In addition to more commonly known online lenders that lend in the United States, one firm is using an algorithmic approach to data analysis and has expanded to overseas markets, particularly China, where the majority of borrowers do not have credit scores. Another firm, based in London, is working to provide credit scores for individuals with ‘thin’ credit files, using its algorithms and alternative data sources to review loan applications rejected by lenders for potential errors. Additionally, some companies are drawing on the vast amounts of data housed at traditional banks to integrate mobile banking apps with bank data and AI to assist with financial management and make financial projections, which may be first steps to developing a credit history.
There are a number of advantages and disadvantages to using AI in credit scoring models. AI allows massive amounts of data to be analysed very quickly. As a result, it could yield credit scoring policies that can handle a broader range of credit inputs, lowering the cost of assessing credit risks for certain individuals, and increasing the number of individuals for whom firms can measure credit risk. An example of the application of big data to credit scoring could include the assessment of non-credit bill payments, such as the timely payment of cell phone and other utility bills, in combination with other data. Additionally, people without a credit history or credit score may be able to get a loan or a credit card due to AI, where a lack of credit history has traditionally been a constraining factor as alternative indicators of the likelihood to repay have been lacking in conventional credit scoring models.
However, the use of complex algorithms could result in a lack of transparency to consumers. This ‘black box’ aspect of machine learning algorithms may in turn raise concerns. When using machine learning to assign credit scores make credit decisions, it is generally more difficult to provide consumers, auditors, and supervisors with an explanation of a credit score and resulting credit decision if challenged.
One of the more important sections of the EU’s groundbreaking General Data Protection Regulation (GDPR) focuses on the right to explanation. Essentially, it mandates that users be able to demand the data behind the algorithmic decisions made for them, including in recommendation systems, credit and insurance risk systems, advertising programs, and social networks. In doing so, it tackles “intentional concealment” by corporations.
Additionally, some argue that the use of new alternative data sources, such as online behavior or non-traditional financial information, could introduce bias into the credit decision.Specifically, consumer advocacy groups point out that machine learning tools can yield combinations of borrower characteristics that simply predict race or gender, factors that fair lending laws prohibit considering in many jurisdictions . These algorithms might rate a borrower from an ethnic minority at higher risk of default because similar borrowers have traditionally been given less favorable loan conditions.
The availability of historical data across a range of borrowers and loan products is key to the performance of these tools. Likewise, the availability, quality, and reliability of data on borrower-product performance across a wide range of financial circumstances is also key to the performance of these risk models. Also, the lack of data on new AI and machine learning models, and the lack of information about the performance of these models in a variety of financial cycles, has been noted by some authorities.
Use for pricing, marketing and managing insurance policies
The insurance industry is using machine learning to analyse complex data to lower costs and improve profitability. Since analyzing data to drive pricing forms the core of insurance business, insurance-related technology, sometimes called ‘InsurTech,’ often relies on analysis of big data. Adoption of AI and machine learning applications in InsurTech is particularly high in the United States, UK, Germany and China.
Many applications involve improvements to the underwriting process, assisting agents in sorting through vast data sets that insurance companies have collected to identify cases that pose higher risk, potentially reducing claims and improving profitability. Some insurance companies are actively using machine learning to improve the pricing or marketing of insurance products by incorporating real-time, highly granular data, such as online shopping behaviour or telemetrics (sensors in connected devices, such as car odometers). Firms usually have access to those data through partnerships, acquisitions, or noninsurance activities. In many cases, firms need to ask for an active consent of the user whenever data protection regulation asks them to.
AI and machine learning applications can substantially augment some insurance sector functions, such as underwriting and claims processing. In underwriting, large commercial underwriting and life or disability underwriting can be augmented by AI systems based on NLP.
These applications can learn from training sets of past claims to highlight key considerations for human decision-makers. Machine learning techniques can be used to determine repair costs and automatically categorise the severity of vehicle accident damage.In addition, AI may help reduce claims processing times and operational costs. Insurance companies are also exploring how AI and machine learning and remote sensors (connected through the ‘internet of things’) can detect, and in some cases prevent, insurable incidents before they occur, such as chemical spills or car accidents.
It seems likely that these methods will achieve greater adoption. According to private sector estimates, global InsurTech investment totalled $1.7 billion in 2016. At the same time, 26 per cent of insurers provide monetary or non-monetary support (for example, coaching) to digital start-ups, and 17 per cent of insurers have an in-house venture capital fund or investment vehicle targeting technology. While the use of machine learning has the potential to produce more accurate pricing and risk assessment for insurance companies, there may be consumer protection concerns that stem from potential data errors or the exclusion of some groups.
Chatbots are virtual assistants that help customers transact or solve problems. These automated programmes use NLP to interact with clients in natural language (by text or voice), and use machine learning algorithms to improve over time. Chatbots are being introduced by a range of financial services firms, often in their mobile apps or social media. While many are still in the trial phase, there is potential for growth as chatbots gain increasing usage, especially among the younger generations, and become more sophisticated.
The current generation of chatbots in use by financial services firms is simple, generally providing balance information or alerts to customers, or answering simple questions. It is worth observing that the increasing usage of chatbots is correlated with the increased usage of messaging applications.
Chatbots are increasingly moving toward giving advice and prompting customers to act. In addition to assisting customers of financial institutions in making financial decisions, financial institutions can benefit by gaining information about their customers based on interactions with chatbots. While outdated infrastructure for client data storage has slowed the development of chatbots in financial institutions in many jurisdictions, Asian financial institutions and regulators have developed more sophisticated chatbots that are currently in active use. The insurance industry has also explored the use of chatbots to provide real-time insurance advice