最有看点的互联网金融门户

最有看点的互联网金融门户
全新的互联网金融模式国际资讯

金融科技助人脱贫,风险可曾充分评估?

全新的互联网金融模式国际资讯

金融科技助人脱贫,风险可曾充分评估?

金融技术或金融科技能够帮助未享受金融服务人群,这一点毋庸置疑。目前没有银行账户、储蓄、贷款和不能使用支付服务的人口数量高达20亿,金融科技的出现让人欢欣鼓舞。

我们认为,金融科技可以帮助低收入人群增强抵御风险的能力、建立资产、管理现金流、增加收入。在过去几年里,Bamboo Capital先后对哥伦比亚、墨西哥、智利和坦桑尼亚的金融科技公司进行了四次股权投资,累计投资额超过1600万美元。这些获得投资的创企通过各种各样的方式推动着当地普惠金融产业的发展,P2P贷款平台KuboFinanciero促进融资大众化,ComparaOnline拓展了保险产品的用户渠道,Movii为低收入人群提供移动支付和储蓄服务,而First Access则为新兴市场金融机构提供智能数据服务。

然而,金融科技的发展与风险并存。人工智能(AI)故障、个人数据挖掘、黑客攻击、身份盗用以及激进的数字信用服务不仅会影响富人和活跃人士,也会给低收入客户带来负面影响,即使一个农民只使用基本型手机也会受到金融风险的威胁。因此揭示创效投资的潜在负面影响对这些客户至关重要。

与高端用户相比,低收入客户由于识字率和计算能力低、对数据保护权的认知程度低、申诉性差、可选项少以及资产和储蓄少等因素,反而更容易受到金融科技服务滥用的伤害。

人工智能和高技术个性化客户服务的缺失

类似于"传统小额信贷"交易,"高技术个性化"客户服务是指人工参与度高的服务。当一个人带着信贷专员拜访客户,整个活动中会有高度人工参与。人类交易以信托为基础:借款人信任 贷款人将对其信用进行公平评估,并尊重交易的条款和条件;贷方把资金的本金和利息托付给借款人。每天通过握手、签名和眼神交流进行的交易行为高达数十亿次。对于低收入消费者来说,人工接触尤其重要,因为对个人的信任比对机构的信任更重要。

当情况变成无须当面沟通、通过设备完成贷款,会有什么不同呢?纯数字交易重新定义了信任关系和双方承诺。移动供应商授权数字贷款时,参与者从两个变成三个,而且互不相见。基于算法的贷款支持者认为,整个过程消除了决策中的主观因素,取而代之的是基于数据的决策。但自适应数字交易可能导致过度标准化,对还款能力的分析可能不够严谨,或被AI驱动的算法取代。算法还是信贷专员?究竟哪一种能够提供更好、更公平的判断?我们只知道必须充分审查债务负担和还款能力。反之则可能导致过度借贷和客户过度负债,或基于不透明推理,包括基于位置等因素的任意分析,导致贷款请求被拒绝。由于数字金融服务越来越多地将目光对准低收入阶层,因此必须确保不会忽略适用性原则,即"只出售客户可以使用和需要的东西",这一点十分重要。

财务透明至关重要

无论是传统金融服务,还是金融科技服务,透明度都是客户保护的另一个重要方面。算法是很少公开的有价商业财产,传统的贷款政策更加透明。就像T恤标签没有足够地方罗列生产造成的二氧化碳排放量一样,移动设备空间有限,无法公布相关条款、个人数据使用、违约后果和申诉机制信息。有时顾客甚至不知道自己已经贷款。当信用部们信用修复有困难、成本高且速度慢的时候,人工智能会自动将客户拉入黑名单。因此有报道指出,肯尼亚有超过50万人因为小额贷款而被列入黑名单,金额低至1美元。不幸的是,从黑名单里移除之前,他们将无法获得任何贷款。

数字贷款过程中,当客户贷款申请被使用"备选数据"(包括地理位置、短信使用频率、电话费用、医疗记录,或者浏览记录、社交媒体情况和在线购买记录等互联网理解能力指标)的人工智能平台拒绝时,客户有哪些追索权?谁在进行这种自动化决策?由于人工智能故障导致出现的问题,可能更难追索赔偿。

金融科技领域在客户保护方面还存在其他风险,包括个人数据滥用和安全性破坏,具体包括隐私问题或未授权操作、技术和网络风险、缺乏客户身份认证、密码误用、监管框架薄弱以及执法和赔偿不力。

确保屏幕背后的行为符合道德规范

对于不与客户面对面交流的销售人员,确保行为道德是很难的。假设销售人员收到两条相互矛盾的指示:a)本月放出大量资金,并且b)认真做好并对客户的信誉进行判断。因为目标激进,销售人员会对这两条指令进行折衷处理。金融科技是否会减少或加剧激进的销售目标?现在说可能为时过早。当发生冲突时,大多数人只有在被监视状态才会表现出符合道德规范的行为。Bibi Mehtab Rose-Palan在最近的一篇论文中引用了富国银行的例子,在未经用户同意的情况下,银行以客户名义开设了至少有两百万个存款和储蓄账户。Rose-Palan认为虽然银行号召员工遵守道德规范,但是设定的激进目标导致员工进行欺诈、"道德沦丧"。在这方面投资者责任重大:设定不现实的高目标,就是变相鼓励员工在工作中走捷径。例如,不监视员工行为(对待客户是否友善),而是监控员工绩效(是否达到销售目标),大多数员工就会只专注于销售目标。反之,如果在整个董事会、高级管理层和销售人员中推行道德领导,这种风险就会大大降低。

关注终端用户

作为金融科技的投资者和供应商,我们有责任确定、承认并减轻服务对象面对的潜在负面风险。小额信贷行业经常忽视过度负债等风险。这不是呼吁停止创新,金融科技将成为金融包容最后一英里解决方案,但我们想要的是有责任的金融科技。近年来,一些金融科技消费者保护举措正在浮出水面。与投资者相关的是负责任的金融科技指导方针。有责任心的金融科技公司能够质疑假设,并检查事情可能出错的地方以及如何出错,也会关注终端用户,关注低收入客户特有的脆弱性,为他们负责,给予尊重,拥有良好的判断力。请记住,人工智能永远不能代替人类的同情心和人的判断力。

Financial technology has the potential to help lift millions out of poverty. But are we adequately assessing its risks?

The potential for financial technology, or fintech, to help the financially excluded populations of the world is well documented. As two billion people are still without bank accounts, savings, loans, and access to payment services, fintech is indeed a welcome innovation. At Bamboo Capital Partners, we believe fintech can help low-income people reduce vulnerabilities, build assets, manage cash flow, and increase income, and we have invested as such: In the last couple of years, we have made four equity investments in fintech companies in Colombia, Mexico, Chile, and Tanzania, committing more than $16 million. Our investees are helping democratize access to finance through peer-to-peer lending platforms (KuboFinanciero), promoting access to insurance (ComparaOnline), enabling mobile payments and savings for low-income people through nano deposits (Movii), and providing a smart data platform for emerging market financial institutions (First Access).

Yet fintech doesn’t come without risk. Artificial intelligence (AI) failures, personal data mining, hacking, identity theft, and aggressive digital credit offers affect not only the rich and hyper-connected, but also low-income customers. A rural villager using a basic mobile phone is also exposed to fintech risks. So uncovering the potential negative impacts of impact investing is crucial for these customers.

Low-income customers produce less of a digital trail than do higher-end users, but factors such as low literacy and numeracy, low awareness of data protection rights, little representation, reduced options, and reduced assets and savings compound their vulnerability to abuse.

AI and the loss of high-touch customer service

A “high touch” customer service is a service that has high human involvement, as is the case with traditional microfinance transactions. Anyone who accompanies a loan officer while visiting clients will witnesses the high level of human involvement. Human transactions are based on trust: The borrower trusts that the lender will conduct a fair assessment of their credit worthiness, and will respect the terms and conditions of the deal. The lender trusts the borrower with their money, along with the principal and interest. Every day billions of transactions are sealed with a handshake, a signature, and an eye-to-eye exchange. The human touch is particularly important for low-income customers, where faith in the individual is greater than the faith in an institution.

So what happens when a customer obtains a loan through a faceless device instead? A digital-only transaction redefines the trust relationship and the commitment on both ends. Moreover, when a digital loan is granted through a mobile provider, there are no longer just two but three parties involved. None of them sees the other. Proponents of algorithm-based lending argue this eliminates the subjectivity factor in decision-making, replacing it with data-based decisions. But digital transactions with automated on-boarding may result in excessive standardization. The repayment capacity analysis may be lax or replaced by AI-driven algorithms. Which of the two delivers a better, fairer judgment: an algorithm or a loan officer? What we know is that debt burden and repayment capacity must be adequately scrutinized. If this is not the case, it can lead to over-lending and customer over-indebtedness, or rejection of a loan based on opaque reasoning, including arbitrary profiling based on factors such as location. As frictionless financial services are increasingly targeting those in the low-income bracket, it is paramount to ensure that we don’t overlook suitability principles such as “sell only what the clients can use and need.”

The value of transparency in finance

Another important aspect of client protection in both traditional finance and fintech is transparency. Algorithms are valuable commercial property that are rarely disclosed. Traditional lending policies are more transparent. Just as a T-shirt label doesn’t have enough space to list the CO2 emissions level of its production, there is limited space on mobile devices to disclose information regarding terms, use of personal data, default consequences, and grievance mechanisms. At times customers are not even aware that they have consented to a loan. AI can lead to automatic blacklisting from credit bureaus for which repair is difficult, costly, and slow. Some reports indicate more than half a million people are blacklisted in Kenya for amounts as small as one US dollar—and unfortunately, they will not obtain any loans until they are cleared (if and when).

In digital lending, when the customer’s loan request is rejected by AI using “alternative data”—which may include geolocation, frequency of SMS use, phone charging, medical records, or, for the more Internet-savvy, browsing history, social media profiles, and online purchasing records—what recourse does the customer have? Who is behind this automation-based decision? Redress in case of AI errors may prove harder to obtain.

There are other risks affecting client protection in fintech. These include abuse and breaches of personal data and security, including privacy or failure to obtain prior consent; technology and network risk; deficient customer identity authentication; misuse of passcodes; and weak regulatory frameworks and poor law enforcement and redress.

Ensuring ethical behavior from behind a screen

Ensuring ethical behavior from a sales person who does not see their client in person is hard. What happens when the sales person is under pressure to deliver aggressive targets? Suppose you receive two conflicting instructions: a) Place a large amount of money this month, and b) do it carefully and with good judgment on customers’ credit worthiness. Aggressive targets mean there will be a trade-off between the two instructions received. Will fintech reduce or exacerbate aggressive sales targets? It may be too early to say. When in conflict, most of us behave ethically only when observed. Case in point: In a recent paper, Bibi Mehtab Rose-Palan cites the example of Wells Fargo, where at least two million deposit and savings accounts were opened in the names of customers without their consent. Rose-Palan concludes the “morality diminishing” factor that led the employees to conduct this fraud was the aggressive sales targets set by the same company that called on them to behave ethically. This is a critical responsibility of investors: Set unrealistically high targets, and you will encourage staff to take behavioral short cuts. For example, if you don’t monitor staff behavior (did they treat your client well?) but do monitor staff performance (did they reach the sales target?), most employees will likely focus on the sales goal. If you instead promote ethical leadership across the board, senior management, and sales force, you will reduce this risk.

Focus on the end user

As investors and providers of fintech, we have the responsibility to identify, acknowledge, and mitigate potential negative risks on the very population we aim to serve. The microcredit industry frequently overlooks risks like over-indebtedness. This is not a call to halt innovation. Fintech will be the solution to the last mile of financial inclusion. But responsible fintech is what we want. Recent years have seen a number of fintech consumer protection initiatives surfacing. Of particular relevance to investors are guidelines for responsible fintech. The ability to question our assumptions, and check where and how things might go wrong, are characteristics of responsible players. So is staying focused on the end user—taking account of the specific vulnerabilities of the low-income customer, remaining accountable primarily to them, and exercising respect and good judgment. Remember, AI cannot substitute for human empathy and human judgement.


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