So much has changed in the past ten years… unfortunately, most traditional banks have been slow to adapt.
In parallel with traditional institutions of the banking sector, a thriving fintech industry has flourished, challenging older hierarchies and appealing to the masses with innovative and exciting new solutions. After decades of a relatively stable banking landscape, developments in AI, connectivity, blockchain, and general banking regulation have brought about massive change.
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With new lenders entering the market, consumers now have more choice than ever. App-based debit cards and peer-to-peer lending are just two of the new innovations being tested on a daily basis, and public awareness of these alternative services is growing.
In consequence, risk assessment methods that don’t use AI are falling behind at a rapid pace. Banks must become more agile and reactive to the changing landscape if they are going to keep up with new technology, changing regulations, and disruptions from emerging fintech.
Antiquated Business Practices Costing Money
The question banks need to be asking is whether they can actually afford not to adopt new technologies, AI based. The banking sector has been slow to adapt to the rising threats and opportunities of fintech and they are now faced with more competition than ever. The greatest concerns for risk modeling today are:
- Data Management and Sharing: With increased data regulation and more data in general to sort through, data management and harvesting is a major task. A recent study found 90% of banks have major concerns about data management and 42% specifically stated issues creating an integrated, consistent view of data across the organization.
- Legacy Solutions: The same study found 70% of banks have issues with multiple legacy products built on outdated technology. Their outdated internal systems and infrastructure add further inefficiencies and slow down the risk modeling process considerably.
- Regulatory Change: A fifth of banks (19%) also struggle to keep pace with the rate of regulatory change. As AI-Based risk modeling becomes more common, banks must find a platform that can maintain a level of explainability and transparency necessary for compliance.
Pressure to Increase Agility of Risk Modeling
Most of the challenges facing risk modeling today come back to a lack of business agility. The current process requires input from the innovation hub, databases department, IT department, engineers, data analysts and risk modeling experts . Each includes separate stages with lengthy waiting times. With poor data management practices, outdated technology, and complex internal processes, banks struggle to keep up with the pace of change.
Meanwhile, there is increased pressure from the business units to release more products to extract more business from their current customer base and build a bigger market share. It is very much in the hands of the risk modeling department to close the circle as fast as possible to allow the bank/FI to offer more financial products and increase their business or at least defend their existing business against emerging competition.
However, those financial products / services each require new data schemes, data visibility, regulatory compliance and risk models. Without the technology in place to rapidly respond to these needs, the risk modeling department becomes a hindrance to the bank’s growth and agility.
Staying Competitive With AI Based Credit Risk Modeling
One of the ways to stay ahead of the game is overhauling outdated credit modeling practices.
AI based credit risk modeling makes use of machine learning. It allows the platform to learn as it consumes more information and allows the bank to learn about users’ spending behavior and predict the likelihood of them repaying a loan within a given timeframe.
Increased Business Agility
Risk modeling processes leveraging AI are streamlined and made more efficient, with department staff applying new models in days rather than months. Applying models is as simple as selecting and deploying with the click of a button, without interference or delays typically created by the involvement of bank departments in the traditional modeling process.
An AI-based risk modeling platform allows you to collect data and build models without requiring without the need to wait for execution by multiple departments. Risk modelers can create a hypothesis, apply the model, and validate all in one day and at a reduced cost in terms of man-hours. Models can be prepared within a week, and with the validation team using the same system, all the relevant staff are on board at the same time.
End-to-end Platform Benefits
An all-inclusive platform with suggested features is the best option for most situations, allowing banks to take advantage of tried and tested features. A risk modeling platform with machine learning will be able to suggest new features based on data input, letting the system improve organically over time.
Platforms also allow all relevant parties within a bank or financial institution to collaborate on the same system rather than trying to disseminate the process in separate, unconnected stages between different team members or departments.
By continuously analyzing new data and offering new models to the credit risk professional, in a fully explainable way, a modern credit modeling platform offers exciting opportunity for the bank to become agile and reactive to new opportunities and threats.
Business Strategy and Risk Strategies in Harmony
In order to compete in the new world of 2020 fintech, banks need to become more agile and reactive to emerging trends. In order to do so, every business unit – and the technology that supports them – must be in harmony.
The benefit of an AI-based credit risk modeling platform is that it works for everyone. The financial institutions will have the data needed to create tailored risk assessments in less time, business units get to brainstorm and test more products with greater efficiency, and risk units gain access to a growing database of increasingly accurate and informative data.
Beyond taking better care of a banks current market share, an automated solution allows for accelerated growth, introducing new products at a faster pace, reaching more customers, and learning from their behavior. This is why AI is taking the banking sector by storm and separating the forward thinkers from those unwilling to change with the times.
Ready to provide your bank the agility needed to thrive in today’s market? Schedule a consultation session with a credit modeling expert today.