credit and identity theft charles m. kahn and william roberds
TRANSCRIPT
![Page 1: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/1.jpg)
Credit and Identity Theft
Charles M. Kahn
and
William Roberds
![Page 2: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/2.jpg)
Basic idea of paper
• Present theory of fraud, including ID theft, in credit transactions
• ID theft arises endogenously as a form of opportunistic behavior
• Outgrowth of Kahn, McAndrews, and Roberds (IER 2005)
![Page 3: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/3.jpg)
Fraud risk vs. credit risk
• Payments fraud has grown with the use of electronic forms of payment (FTC report: 12% victimized)
• Fraud risk quite small compared to credit risk (for credit cards, 5 BP versus 400 BP)
• Nonetheless, fraud risk a critical concern for industry
![Page 4: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/4.jpg)
Modeling identity
• Usually modeled as history of agents’ actions
• We must go further: problem is to link a particular history with individual making a current transaction
![Page 5: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/5.jpg)
Modeling identity
• Individual’s identity will be denoted by a unique (infinite) sequence of ones and zeros.
• We will describe technology for distinguishing an individual from an impersonator
![Page 6: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/6.jpg)
Modeling identity
• In his role as a producer an individual’s identity is unproblematic
• The difficulty is to link the production history with a particular attempt at consumption
![Page 7: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/7.jpg)
The framework
• N agents, infinitely lived, risk neutral, with common discount factor
• Each agent identified with a “location” where he can produce a unique, specialized, non-storable good at a cost s
![Page 8: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/8.jpg)
The framework
• Each period one agent wakes up “hungry” for the good of a particular producer
• Consumption of that good by that agent provides him utility u; any other consumption in the period gives the consumer 0 utility
![Page 9: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/9.jpg)
The framework
• Note: no double coincidence of wants
• Therefore no possibility of barter (if s > 0)
• Some arrangement needed for intertemporal trade
![Page 10: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/10.jpg)
The framework
• The value of u is common to all agents.
• The value of s is distributed in the population with distribution F
0< F(u) <1
• A producer’s value of s (his “type”) is unchanging over time, and is private information to the producer
![Page 11: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/11.jpg)
The framework
• The hungry agent can travel to the location of his preferred supplier
• The hungry agent’s own location is not automatically revealed
• Refusal to supply a good is observable
![Page 12: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/12.jpg)
Timing
• At time 0, agents learn their own costs, and have the opportunity to form club (binding commitment)
• Agents in club receive goods from club members, but must supply goods to other club members
• denotes fraction of population in club
![Page 13: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/13.jpg)
Enforcement
• Agreements can be enforced by court: assume has power to punish one individual up to an amount X (large), provided he can be identified
• Thus “fraud risk” but no “credit risk”
![Page 14: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/14.jpg)
Events within a period
1. Hungry agent and supplier randomly chosen
2. Hungry agent journeys to supplier’s location
3. Hungry agent’s identity is verified
4. If verification successful, trade occurs
![Page 15: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/15.jpg)
Baseline: costless identification
Result: Provided X > u, all individuals with s < u join club (club size F(u) )
• A member’s expected utility is
V(s) = –1 (u – s)
• Constrained efficient (cross-subsidy not allowed)
![Page 16: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/16.jpg)
Verification technology
• Examine a sample of n bits of individual’s identity at cost k per bit sampled
• No type I error; probability of a false match of zn
• Optimal sampling increases with s and falls with k, z, or
![Page 17: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/17.jpg)
Equilibrium
• Find the cutoff level of supply cost for membership such that – all members join voluntarily and are willing to
supply – each chooses his preferred monitoring sample – all non-members prefer to remain outside the
club (and attempt impersonation)
![Page 18: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/18.jpg)
Credit club equilibrium
Result: If X > u
For small k, equilibria exist with F(u).
As k shrinks, approachesF(u).
![Page 19: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/19.jpg)
Credit card technology
• The credit card is a manufactured “pseudo-identity”: a string of bits, verifiable at lower cost than the identity itself.
• The credit card club makes an initial check of identity, then issues the member a card
• Subsequent suppliers verify the card rather than the person.
![Page 20: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/20.jpg)
Equilibrium
Analogous definition: Agents voluntarily choose between:
• joining the club (being monitored initially, and supplying to all card holders after monitoring their cards)
• not joining (not supplying, instead attempting credit card fraud)
![Page 21: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/21.jpg)
Types of fraud
• Thus either the card or the person can be imitated: “existing account” vs. “new account” fraud
![Page 22: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/22.jpg)
Key result
• Equilibrium with credit cards exists under same conditions as before, and dominates equilibrium with independent verification of buyers
![Page 23: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/23.jpg)
Benefits of card arrangement
• Cards allow club members to share information gleaned in initial screening
• More intense verification possible; more frauds are excluded
• Size of club expands; high cost producers induced to join club
![Page 24: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/24.jpg)
Costs of card arrangement
• Shared information info on buyer IDs may be incorrect, leading to– New account fraud– Existing account fraud
![Page 25: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/25.jpg)
Policy implications
• Popular notion is sometimes advanced that more sophisticated cards can “solve the problem” of ID theft—
• But more sophisticated cards may actually contribute to the problem by making credit card payment more prevalent, increasing incentives for fraud.
![Page 26: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/26.jpg)
Policy implications
• Proposed privacy legislation may also fail to curb ID theft—
• By constraining ID samples, such legislation may encourage new account fraud (“impersonation” in the model)
![Page 27: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/27.jpg)
Policy implications
Key policy tradeoff
• Gathering & sharing more information on buyers leads to better allocation of credit, but
• Amassing such data necessarily entails private & social costs (loss of privacy)
![Page 28: Credit and Identity Theft Charles M. Kahn and William Roberds](https://reader036.vdocuments.us/reader036/viewer/2022081513/56649ca35503460f949632f3/html5/thumbnails/28.jpg)
Key ideas of paper
• Credit-based transactions systems are systems for efficiently sharing information among sellers (esp. ID of buyers)
• ID theft a problem because it exploits exactly this source of efficiency
• Transactions systems must balance costs of fraud against costs (private & social) of ID verification