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A second opportunity is to help SMEs and mid-market corporates understand their position. Many lack a finance department equipped to run cashflow forecasts and scenarios, and therefore may not have robust projections on which to base their decisions. Banks can help their customers forecast cashflows under different macro scenarios and thereby understand their funding runway and the timing of mitigating actions.
Some banks are looking to adapt existing cashflow forecasting tools for this purpose, and a number of fintech solutions that already exist could be also adapted for this purpose. The third priority should be helping clients navigate the funding and restructuring solutions available to them. These range from claims for COVID-related grants through to government lending schemes and payroll subsidy schemes. Beyond near-term funding, there will be opportunities to support customers in restructuring their businesses e.
Consistency when engaging with clients will be necessary to avoid relationship and reputational damage, while advice must be continually updated to reflect the latest government policies and customer feedback. Ensuring that everyone is on message and behaving in line with the engagement strategy will be challenging. There may be a case for establishing a dedicated restructuring taskforce to coordinate and manage RM communications and directly support customer discussions.
A core role of the Credit War Room will be to adjust and codify the credit decisioning framework to deal with the rapidly evolving situation. In some areas, the new lending criteria could be fully codified, allowing for automated decisions. In others, they will provide a common foundation for expert-based decisions to be prudently simplified and triaged to cope with extraordinary volume of applications.
The foundation of the decision framework will be a central scenario for how the crisis will play out and alternative scenarios. These comprise epidemiological scenarios how will the pandemic play out , macro-economic scenarios the effects on GDP, inflation, sector performance, etc. In parallel, a comprehensive set of client treatment strategies should be developed. These could include increased limits, renewals of existing facilities, additional invoice finance facilities, interest payment holidays and principal repayment holidays.
They should also include options created by government policy initiatives, such as government-backed lending, business rates relief, and sick pay support. In some cases, the decisioning logic can be embedded within existing automated decision engines and processes. In other cases, it will need to be provided to RMs and credit officers in rapidly developed tactical tools and systems.
The Credit War Room would then support RMs and credit officers in interpreting and implementing the decision framework in live client situations, dealing with exceptions and, where necessary, feeding back into the decisioning framework. Much of the challenge will be operational. Many banks are reporting loan application volumes that are eight to 10 times their typical level and likely to grow further as awareness of government-supported loan schemes increases.
This is well above operational capacity, especially given remote working and sickness-related resource constraints. The difficulty is exacerbated by the complex underwriting challenge in a fast-moving environment and the specific process requirements of national guarantee schemes which, given their novelty, remain manual for most banks. Immediate solutions will usually need to include temporary resource augmentation to handle manual applications in contact centers and credit teams, perhaps by redeploying staff doing non-urgent work elsewhere in the bank.
Allowing exceptions to current policies regarding data requirements and process steps will also be expedient when doing so does not create undue risk — for example, where existing data can be relied on. And, of course, wherever possible, client requests should be diverted to online channels. Over the coming weeks, banks will also need to establish a more sustainable approach to processing the surge in lending applications.
This should include electronic channels to manage COVID related loan applications either as a separate channel or as a separate track within existing channels and the extension of automated credit decision-making to as many applications as possible, allowing manual credit underwriting to be focused on exceptions and higher risk cases.
This automation may require some banks to increase their API connectivity to data sources and to the government agencies involved financial support schemes. The increased use of electronic signatures and pre-approval of credit limits for existing clients based on a sector-specific view of distress can also expedite credit processes. In parallel with dealing with inbound requests for additional finance, banks will need to understand the impact of the crisis on their existing loan portfolios.
They will inevitably suffer credit losses that reduce the profits of the corporate banking businesses, as well as its capital and lending capacity. Understanding the portfolio impacts will require a joint effort between the business and credit analytics teams to identify stressed sectors and clients, through a combination of expert judgment, early warning signals and public data such as equity and CDS prices.
This is likely a symptom of the way business leaders think about AI today: As regular IT, as plug-and-play software. Each of these facets of a business varies greatly from department to department, business function to business function. As such, each of them requires a separate strategy to tackle the unique problems within them, problems AI may or may not solve now and may or may not solve in the coming decade.
Businesses also need to align their Ai strategy with their business strategy within each functional division. If our strategy is to recommend better products to existing customers and grow our customer base by finding customers that are already doing business with our existing customers, what capabilities can AI offer to enhance that strategy that were never available before?
This contrasts with the way banking leaders today are talking about AI in the C-suite. Wilson provides further detail to this dynamic when he says:. What comes next? Where is the ROI? It was just throw some mud at the wall and see what sticks. For example, Wilson discusses how banks struggle to deal with all of their paper documents.
Banks may want to look for NLP and machine vision software for digitizing paper documents and allowing for document search capabilities. We discuss the important role the customer experience will play in determining the future of banking later in the article.
Our interviewees highlighted three core challenges to adopting AI in banking:. AI initiatives come with a lot of uncertainty, and banks are not used to experimenting to achieve ROI. Banks that want to survive in the coming years will need to cultivate a culture of innovation within their business, starting with their leadership teams. Banks want to know that AI will work, but according to Wilson:. Wilson believes banks will overcome this hurdle when vendors can start telling them their competitors are using particular AI applications.
In the next few years, banks will need to learn to accept the risk associated with AI if they intend to compete with other banks and fintech companies. They need to understand that, as previously discussed, an AI initiative may not ever generate an ROI let alone generate one in several years. To do this, they should focus on both hiring data scientists in-house and educating their leadership teams on AI adoption best practices.
Our interviewees mentioned the importance of leadership in cultivating the company culture required for successful AI implementation. Chandra, in particular, said the following:. Leaders make sure the stars get aligned. That is the core premise of a great leader. They become the guards who align the stars in the organization. What we have to do at [the leadership] level is basically find out the top 5 challenges and the top 5 things we can do to [overcome] those challenges…When you rate the top 5 on your left and top 5 on your right, you are basically matching columns.
The culture of everyone taking ownership and doing something like this in every level of an organization, that is the culture which will basically transform the companies. The companies are not transformed by the leaders, the company is transformed by the culture which leaders bring in. Smallwood echoes this sentiment and emphasizes the importance of AI-focused talent at the leadership level:.
The number one thing for me is talent. Chandra also gives an example of how the leadership team can steer a company away from embracing the culture necessary for adopting AI. He gives an example of a company in which the technology infrastructure was ready and capable of handling an AI upgrade, but the leadership team would not pull the trigger on an initiative:. An organization had to have spent a lot of money to be ready for transformation or…the inception of the organization happened in the digital domain…The leadership may fail to create value out of data: how do I monetize it?
How do I leverage it toward barriers for entry for my competitors? How do I use this for a target advertisement for our product? So the question becomes the leadership is sitting on this massive amount of data. This scenario harkens back to the importance of a coherent AI strategy for a particular business function. Leaders need to educate themselves on the capabilities of AI so they can craft these strategies and be able to answer the kinds of problem-solving questions necessary for working with their data science teams to iterate and experiment with AI.
Only then will a culture of innovation start taking hold within an organization. Chandra points at how the current approach to integrating new technologies, AI in particular, needs to change in the coming years for banks to remain competitive. One way of going about this could be embracing open source technologies.
Banks are used to using licensed software from software vendors, but open-source software is becoming more ubiquitous, and it may save banks on cost and on the valuable time they could be spending catching up with their competitors. According to Nishant Chandra:.
Now [banks] realize that working on legacy domains limits their capabilities in various ways. It limits their capabilities in terms of using the power of their big data. It limits…the speed at which they can respond. It also ties them to certain organizations and their release portfolios. Chandra points out how open source technologies are in many cases releasing faster than licensed software, and they also tend to be more agile and flexible.
That said, banks face serious challenges when transitioning from their legacy licensed software to open source software. In many cases, this transition relates to a cultural challenge at the bank, as discussed previously. Leadership teams are often reluctant to invest in workflow overhauls that may not result in any ROI or cost savings for a very long time.
Chandra illuminates this point when he says:. The potential payoffs would take up to 5 years if not a decade. Its an investment but its an investment that would be continued, meaning new open source software will continue to come up. AI is in large part the reason fintech companies get to compete with banks for market share of key banking functions. Banks are no longer the only place consumers can go to receive banking services.
Numerous tech startups are offering financing options and money transfer services to customers entirely over the internet. Gribov believes:. We have a lot of things to be afraid of because a lot of the things banks are doing can now be done without any bank. You can send money with messaging apps.
This poses a threat to banks that have historically relied on face-to-face interactions with customers at local branches, and this threat will grow in such a way that it will force banks to not only adopt AI but to become leaner in the next decade. Banks may not be able to compete with Fintech companies in the customer experience domain.
Fintech companies may be able to offer better customer experiences to younger customers used to digital banking and digital communications. According to Gribov, fintech companies built on AI algorithms can:. AI can provide way better service than a human ever could.
Customer experience is really what wins regardless of the age or profile of who that consumer is. Yes, in some aspects of finance, that may be a factor. Banks take on greater risk than small fintech startups; as such, compliance is a much greater concern for banks right now, probably as it should be.
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