Small and medium sized enterprises (SMEs) account for approximately 98 percent of the industry and contribute more than 50 percent of non-oil GDP in the UAE. Given that these SMEs have a sizeable economic impact, sustainable and continuous growth of these businesses is critical for the wellbeing of the economy.
The main driver for SME growth is access to financing for supporting their working capital needs, inventory purchase, revenue seasonality and business expansion. Sourcing this financing is a fundamental challenge faced by these small entities. Banks and non-banking lenders typically avoid underwriting small and risky loans and, in most cases, conventional methods of assessing credit worthiness will not allow these SMEs to obtain financing via traditional channels.
Bring in AI
Innovations in technology and its implementation in the lending industry provide unique opportunities for the banking sector. The computational efficiency and predictability using data sets that have increased in size, velocity and variety form the basis for machine learning and artificially intelligent lending approaches.
If lenders can cost effectively identify, evaluate, underwrite and monitor SMEs, they can have scalable lending portfolios that can yield substantial spreads. This will lead to better risk adjusted profitability than what banks earn on their consumer products.
The advancements in machine learning and AI could help UAE banks in tapping the unbanked SME segment through optimal selection, effective monitoring, lower costs and improved customer experiences.
Making it SME friendly
There are many possible ways in which use of machine learning and AI can help the local banks to improve their SME lending. The small size and short duration of SME loans require banks to adapt a cost-effective approach for customer acquisition and retention.
The customer acquisition cost is a significant expense and a drag on profitability, especially when the relationship size and maturity is short. The AI and machine learning based models can help banks better manage this expense. From potential customers’ data set, these models can predict the probability of response, and banks can focus on the most relevant and promising leads.
Similarly, these technologies can help in identifying appropriate marketing channels and communications for their existing relationship base and consequently lower overhead costs.
Micro manage the data
Underwriting automation is another aspect of artificial intelligence and machine learning assisted banking that can optimize small business lending. When banks collect data for SME lending, this usually includes qualitative and quantitative information about the business as well as personal data of the promoters.
The relevant information from this data will be used by the banks for loan pricing and exposure mitigation. The challenge is that such data collection is subject to lot of noise and the information relevance may vary across clients and could be costly to process manually. Through machine learning and artificial intelligence models, the underwriting and credit assessment process can be automated.
This can help the bank to cost effectively process the data, focus on relevant variables and create predictive risk metrics to adequately price the loans. Such models can also incorporate unconventional information like digital footsteps of a borrower to arrive at a more reliable credit assessment.
The deal structure for SME lending usually involves a lot of communication between banks and the businesses. This may result in bottlenecks across bank approval systems and sub optimal allocation of human resources, especially if there are many cases to be processed. However, this can be avoided through machine learning approaches that can optimize the workflow and queue the loan approvals dynamically.
Consequently, the opportunities that are more relevant can be prioritized and dealt with earlier. The prioritization could be on the basis of probability of deal closure, lower likelihood of default, etc. This not only improves customers’ experience, but also ensures that lenders focus on the right opportunities.
The growth of SME sector is inevitable for overall economic growth, and the significance of small businesses is constantly on the rise for the UAE economy. This growth requires access to bank financing, which also offers new business opportunities for the local banking sector.
By leveraging on new technologies and building on automation, enhanced risk assessment and optimized workflow, banks in UAE can invest in sustainable SME lending and further improve their risk adjusted performance.
- Dr. Nawazish Mirza is associated with La Rochelle Business School, France.