乌鸦传媒

---

What鈥檚 wrong with this picture?

Sarah and Paul have the bakery they dreamed of, thanks to their bank. But they don鈥檛 thank their bank 鈥� they resent their credit lender deeply. They鈥檝e begun avoiding bank services whenever possible, accepting inconvenience rather than 鈥済etting in deeper.鈥� In conversation with friends and family, Sarah鈥檚 and Paul鈥檚 stories of their experience bring laughter and sympathy. This couple just received a life-changing loan, and they鈥檙e outraged. What explains this disconnect?

The terms of the couple鈥檚 loan are fair 鈥� standard for the industry. But Sarah and Paul didn鈥檛 understand the lending process 鈥� any part of it. They didn鈥檛 understand their bank鈥檚 requests or decisions and felt bombarded by unforeseen questions. Then a small error occurred on the bank鈥檚 side, the decision was delayed with no rational given, and the couple was called in for another long meeting they didn鈥檛 understand. The last of their trust dried up. How can banks avoid situations like this, and strengthen relationships with clients? The next generation of credit-risk solutions make it easier for banks and customers to navigate through complex processes and build lasting trust.

The costs of legacy systems

A surprising number of banks rely on outdated credit risk-assessment systems, mostly for SME and corporates clients, resulting in staggering losses due to errors, reputational damage, credit risk capital charge increases, and regulatory impacts and fines. In these times of uncertainty, actors need to be up to date with best-in-class solutions for their lending process. The consequences of outdated systems can be serious:

• Requiring clients to be present in person drives them to more convenient competitors
• Complex credit processes lack transparency, leading to misinformation and misunderstandings on the client鈥檚 side, and reputational damage to the bank
• Credit decision delays due to manual intervention drive costs up, and clients out
• Credit risk is underestimated 鈥� due to errors or imprecision 鈥� leading to unrecoverable loans
• Credit risk is overestimated, leading to customer attrition

Allocating more manpower to credit risk hasn鈥檛 helped. Legacy systems are preventing banks from solving problems 鈥� whether that means adapting to new regulations, taking advantage of new opportunities, coping with credit risk level or most importantly, creating the seamless customer experience which people have become accustomed to. This is where next-gen credit risk solutions come in. Placing agility at the heart of a bank鈥檚 operations and simplifying the credit risk granting process, they provide the foundation for forward movement.

The difference next-gen makes

Caleb is a banking broker who loves his job. But he didn鈥檛 always. Last year he spent his free time looking for a new career. He was exhausted 鈥� and hurt 鈥� by customer complaints on one side, and unrealistic expectations from his bank on the other. He suffered from an impossible workload. His system鈥檚 incapacity to reach automated decisions and the frequency of poor automated decisions led to constant manual interventions. He had to navigate complex financial analysis, compliance regulations, and often felt uncertain about which data to use in the systems and how to calculate it. The result of his efforts? Disciplinary action from above due to incorrect calculations.

Now with his bank鈥檚 new credit risk system, Caleb finally feels good about his work. He works with an AI-enhanced credit decision system which walks him and his clients from the initial application all the way through to the final approval or rejection. Caleb doesn鈥檛 have to worry anymore about which data to use, as the systems automate data gathering and generate 1^st level analysis. A specialized credit decision assistant enables both him, and his customers, to better understand complexity. Integrated end-to-end internally and with key external sources, it speeds up the work, reduces the risk of error, and lowers the credit risk impact for the bank.

It also provides him and risk managers with real-time analysis on complex requests, further automating the credit process. Today this integrated system automatically draws information from multiple, as yet unused external data sources, without any action (or room for error) as it鈥檚 processed and fed directly into the decision process. In areas where a human touch is helpful, this system takes the credit process as far as possible. Pre-processing of decision-making leaves Caleb and risk managers with only the most critical elements of a previously painstaking task. Risk managers are thus able to provide faster and more accurate risk analysis, and brokers can focus on their relationships with customers.

From transparency to trust

There are multiple reasons why people don鈥檛 trust their banks, but one overarching solution 鈥� people greatly want to understand the processes they鈥檙e getting involved in. Taking out a loan is one of the highest-stakes decisions that people make in their lives, and unfortunately, also among the most mystifying. A next-gen credit risk puts clarity at the core of your bank鈥檚 systems, pulling aside the curtains for employees and customers alike. They benefit from a credit risk assessment process that is quicker, more accurate, simpler, and more human. AI and data management reduce the risk of fraud, errors, and the credit risk impact 鈥� saving banks money, and saving honest customers much of the hassle and discomfort of financial scrutiny. Next-gen credit risk helps banks achieve operational excellence throughout the loan application process, and long after 鈥� so that when the next generation of your customers build their dreams, they鈥檒l know who to thank for it.

Author

Senior Managing Consultant, Enterprise Data & Analytics 鈥� 乌鸦传媒 Invent